43
PUBLIC Juergen Haupt, SAP May, 2019 How Big Data and Agility Affect SAP BW4/HANA Architectures SAP User Groups Knowledge Transfer Webinar

SAP User Groups Knowledge Transfer Webinar How Big Data and … · 2019-05-23 · Agile analytics, agile Data Warehousing, agile BI Introducing Agile Analytics: A Value-Driven Approach

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PUBLIC

Juergen Haupt SAP

May 2019

How Big Data and Agility Affect SAP BW4HANA Architectures

SAP User Groups Knowledge Transfer Webinar

2PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP

Except for your obligation to protect confidential information this presentation is not subject to your license agreement or any other service

or subscription agreement with SAP SAP has no obligation to pursue any course of business outlined in this presentation or any related

document or to develop or release any functionality mentioned therein

This presentation or any related document and SAPs strategy and possible future developments products and or platforms directions and

functionality are all subject to change and may be changed by SAP at any time for any reason without notice The information in this

presentation is not a commitment promise or legal obligation to deliver any material code or functionality This presentation is provided

without a warranty of any kind either express or implied including but not limited to the implied warranties of merchantability fitness for a

particular purpose or non-infringement This presentation is for informational purposes and may not be incorporated into a contract SAP

assumes no responsibility for errors or omissions in this presentation except if such damages were caused by SAPrsquos intentional or gross

negligence

All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from

expectations Readers are cautioned not to place undue reliance on these forward-looking statements which speak only as of their dates

and they should not be relied upon in making purchasing decisions

Disclaimer

4PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness Modeling features and patterns supporting flexibility and agility

6PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

SAP BW4HANA with SAP HANA enables

different valid architectures

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

7PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesOverview

EDW ndash Simplified EDW ndash FlexibleRelational Data Lake

evolutionary DWHDWH ndash Agile Architecture

Technology

Process

Ownership

Technology

Process

Architecture

IT Business

Top Down Scrum incremental evolutionary fail earlyBottom Up

Top Down

Big Design Upfront Sufficient Design Upfront

SAP BW4HANASAP HANA

SAP BW4HANA architectures

Simplified EDW with LSA++

9PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Technology

Process

Architecture

Big Design Upfront

Waterfall

EDWLSA++

SAP HANA

SAP BW4HANA

ODP

Top-Down

Virtual

Data Marts

DTO

InfoObjects

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

Data Store

Object

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH IT driven

governance

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

2PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP

Except for your obligation to protect confidential information this presentation is not subject to your license agreement or any other service

or subscription agreement with SAP SAP has no obligation to pursue any course of business outlined in this presentation or any related

document or to develop or release any functionality mentioned therein

This presentation or any related document and SAPs strategy and possible future developments products and or platforms directions and

functionality are all subject to change and may be changed by SAP at any time for any reason without notice The information in this

presentation is not a commitment promise or legal obligation to deliver any material code or functionality This presentation is provided

without a warranty of any kind either express or implied including but not limited to the implied warranties of merchantability fitness for a

particular purpose or non-infringement This presentation is for informational purposes and may not be incorporated into a contract SAP

assumes no responsibility for errors or omissions in this presentation except if such damages were caused by SAPrsquos intentional or gross

negligence

All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from

expectations Readers are cautioned not to place undue reliance on these forward-looking statements which speak only as of their dates

and they should not be relied upon in making purchasing decisions

Disclaimer

4PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness Modeling features and patterns supporting flexibility and agility

6PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

SAP BW4HANA with SAP HANA enables

different valid architectures

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

7PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesOverview

EDW ndash Simplified EDW ndash FlexibleRelational Data Lake

evolutionary DWHDWH ndash Agile Architecture

Technology

Process

Ownership

Technology

Process

Architecture

IT Business

Top Down Scrum incremental evolutionary fail earlyBottom Up

Top Down

Big Design Upfront Sufficient Design Upfront

SAP BW4HANASAP HANA

SAP BW4HANA architectures

Simplified EDW with LSA++

9PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Technology

Process

Architecture

Big Design Upfront

Waterfall

EDWLSA++

SAP HANA

SAP BW4HANA

ODP

Top-Down

Virtual

Data Marts

DTO

InfoObjects

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

Data Store

Object

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH IT driven

governance

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

4PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness Modeling features and patterns supporting flexibility and agility

6PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

SAP BW4HANA with SAP HANA enables

different valid architectures

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

7PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesOverview

EDW ndash Simplified EDW ndash FlexibleRelational Data Lake

evolutionary DWHDWH ndash Agile Architecture

Technology

Process

Ownership

Technology

Process

Architecture

IT Business

Top Down Scrum incremental evolutionary fail earlyBottom Up

Top Down

Big Design Upfront Sufficient Design Upfront

SAP BW4HANASAP HANA

SAP BW4HANA architectures

Simplified EDW with LSA++

9PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Technology

Process

Architecture

Big Design Upfront

Waterfall

EDWLSA++

SAP HANA

SAP BW4HANA

ODP

Top-Down

Virtual

Data Marts

DTO

InfoObjects

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

Data Store

Object

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH IT driven

governance

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

6PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

SAP BW4HANA with SAP HANA enables

different valid architectures

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

7PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesOverview

EDW ndash Simplified EDW ndash FlexibleRelational Data Lake

evolutionary DWHDWH ndash Agile Architecture

Technology

Process

Ownership

Technology

Process

Architecture

IT Business

Top Down Scrum incremental evolutionary fail earlyBottom Up

Top Down

Big Design Upfront Sufficient Design Upfront

SAP BW4HANASAP HANA

SAP BW4HANA architectures

Simplified EDW with LSA++

9PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Technology

Process

Architecture

Big Design Upfront

Waterfall

EDWLSA++

SAP HANA

SAP BW4HANA

ODP

Top-Down

Virtual

Data Marts

DTO

InfoObjects

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

Data Store

Object

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH IT driven

governance

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

7PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesOverview

EDW ndash Simplified EDW ndash FlexibleRelational Data Lake

evolutionary DWHDWH ndash Agile Architecture

Technology

Process

Ownership

Technology

Process

Architecture

IT Business

Top Down Scrum incremental evolutionary fail earlyBottom Up

Top Down

Big Design Upfront Sufficient Design Upfront

SAP BW4HANASAP HANA

SAP BW4HANA architectures

Simplified EDW with LSA++

9PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Technology

Process

Architecture

Big Design Upfront

Waterfall

EDWLSA++

SAP HANA

SAP BW4HANA

ODP

Top-Down

Virtual

Data Marts

DTO

InfoObjects

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

Data Store

Object

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH IT driven

governance

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

SAP BW4HANA architectures

Simplified EDW with LSA++

9PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Technology

Process

Architecture

Big Design Upfront

Waterfall

EDWLSA++

SAP HANA

SAP BW4HANA

ODP

Top-Down

Virtual

Data Marts

DTO

InfoObjects

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

Data Store

Object

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH IT driven

governance

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

9PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Technology

Process

Architecture

Big Design Upfront

Waterfall

EDWLSA++

SAP HANA

SAP BW4HANA

ODP

Top-Down

Virtual

Data Marts

DTO

InfoObjects

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

Data Store

Object

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH IT driven

governance

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

10PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea Consistent integrated data across the

Enterprise designed using SAP BW4HANA

simplification reducing development efforts

High performance staging and querying

Virtualization ndash Less persisted layers

Powerful semantics modeling

ndash Queries CompositeProviders Open ODS Views

InfoObjects

ndash Simplified models new degree of flexibility

ndash Dynamic Star Schema Advanced DSO unified Time

dimension

IT driven central governance

ndash Waterfall Top-Down modeling Big Design Upfront

SAP BW4HANA architecturesEDW ndash simplified architecture with LSA++

LSA++

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

11PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

12PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - virtual data marts

From flat Star Schema to the dynamic Star Schema

Persisted InfoProviders define no longer a star schema in BW4

A (dynamic) star schema is only defined by a CompositeProvider or an Open ODS View (type fact)

Dimensions are defined via associating master data (InfoObjects or Open ODS Views (type master)

with the navigational attributes

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

13PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

CompositeProvider defining a dynamic star schema ndash

associations and navigational attributes

Associate master data (dimensions)

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

SAP BW4HANA architectures

Flexible EDW with LSA++

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

15PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

Technology

Process

Architecture

EDW LSA++

SAP HANA

SAP BW4HANA

ODP

Virtual

Data Marts

DTO

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

InfoObjects

Data Store

Object

Big Design Upfront

Waterfall Top-Down

Bottom-upincremental

LSA++

bo

tto

m u

p m

od

eli

ng

IT driven

governance

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

16PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea make the EDW more responsive ndash

introduce bottom up modeling while having a

consistent integrated data across the Enterprise

as target ndash designed using SAP BW4HANA

simplification further reducing development efforts

Bottom up modeling using Open ODS Layer field

modeling for fast low cost solution increments

Propagation Layer as optional target

Additional persisted layer only if services are

necessary for a solution

Virtual transformations with mixed scenarios

SAP BW4HANA architecturesEDW ndash flexible architecture with LSA++

The stairs of the LSA++ persisted data layer

outline the incremental approach supporting a

responsive development

LSA++

bo

tto

m u

p m

od

eli

ng

Service Level

top

do

wn

mo

de

ling

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

17PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for flexible EDW and agile data warehousing

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

18PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - InfoObject and field-based modeling

Cross layer modeling - integrating top-down and bottom-up modeling

Integration modeling

Map fields to InfoObjects

Function modeling Queries

Schemas

Persisted data staging

InfoObjects Fields

BW4HANA Modeling Options

Top Down modeling

Integration before Function

Bottom-up modeling

Function before Integration

Scalable modeling with InfoObjects and fields or a mixture of both

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

19PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA - modeling the Open ODS Layer (RAW DWH)

Bottom-up ndash low-cost

The simple amp low cost DWH

Source driven entities amp values

Bottom-up modeling

Basic DWH services

Raw DWH Modeling with BW4HANA

Persistency modeling with aDSOs

transaction master text data

Fields (InfoObjects possible)

DataSource as template

Dynamic Dimensional modeling with

Open ODS Views

Assign semantics to aDSOs and fields

ODS View type fact (virtual data mart)

ODS View type master

ODS View type text

SQL-Modeling with HANA Views on aDSOs

possible (Mixed Scenario)

Virtual Data Marts

ODS View type fact

CompositeProvider

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

20PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Recap - Open ODS Views

Modeling raw field data with Open ODS Views

The BW metadata model for field data consists of entities ndash the

Open ODS Views ndash defining

ndash Semantics of sources

(fact master data)

ndash Semantics of source-fields

(characteristic key figurehellip)

ndash Associations to other Open ODS Views

ndash Associations to InfoObjects

ODS Views are view constructs

on various types of source objects

ndash BW aDSOs InfoSource DataSources

ndash DB tables amp SQL HANA views

ndash Virtual tables -HANA Smart Data Access

The source object of an ODS view

can be exchanged

From a BW-OLAP perspective ODS Views can be consumed like

InfoProviders (facts) or InfoObjects (master data text)

Query CompositeProvider HANA Model

HA

NA

DB

aDSOViewTable

aDSO

Query CompositeProvider HANA Model

Open

ODS View

Master data

InfoObject

Open

ODS View

Master data

Open

ODS View

Fact data

InfoObject

Master data

Virtual Data Mart

ViewTable

aDSO

ViewTable

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

Example SAP BW4HANA PoC ndash Insurance Industry

Tables with 100rsquos millions rows from

legacy system uploaded to SAP

BW4HANA

lt week

Meaningful reporting on raw

data(field based models)

Couple of days

Business-standard reporting on raw

data with enriched virtual models

(associated IOBJrsquos)

Few days

Formalized data models (IOBJ based

ADSOrsquos)

Several weeks

Too often this is seen as the starting point

real world

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

Benefits for Business Users

Better BI applications

bull Well equipped to support an agile approach to BI

bull Shorter time-to-market for new development and fixes

bull Openness or ease of integration for sources and for consuming applications

The main contributors to these benefits in SAP BW4HANA are

the ability to use Virtual- and Field-based models and the

ability to use virtual integration for a wide variety of sources

real world

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

23PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

SAP BW4HANA architectures

Agility and BW4HANA architecture

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

25PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Continuous disruptive innovations in

analytics area challenge proven best

practice like EDW and LSA++

Agile self-service analytics

Data lake ideas and vision

Enterprise analytics and the triad of Process Architecture and TechnologyContinuous disruptive innovations in analytics area

Technology Architecture

Big Design

UpfrontWaterfall

EDW

Virtual

Data Marts

LSA++

ODP

Top-DownIT driven

central governance

agile analyticsBusiness-

ownership

bottom-up

incrementality

scrum

dev-ops

Hadoop

Vora

Cloud SaaS

Data Lake

Ingestion layer

SAP Data HubSAP Analytics Cloud

LDW

DTO

Process

Sufficient

Design Upfront

Be faster cheaper

closer to business and customers

Field-

modeling

Open ODS

views

InfoObjects

Mixed

ScenariosSAP BW4HANA

SAP HANA

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

26PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Agility derives from the right combination of business practices and technical capability

Move to real-time solutions ndash the death of waiting

Build incrementally

minus agile data marts underpin agile businesses resulting in the ability of a business to change faster and more profitably

Ensure business ownership ndash the business must take the lead

Realign IT priorities

minus IT empowers business peers with self-service tools platforms and applications

minus reducing and ultimately eliminating the pipeline of data query requests that swamp IT

minus without losing the key values that IT delivers such as data consistency security etc

Agile AnalyticsAgile analytics agile Data Warehousing agile BI

Introducing Agile Analytics A Value-

Driven Approach to Business

Intelligence and Data Warehousing

by Ken W Collier ndash Sep 6 2011

SAP Analytics Cloud

Self Service BI

SAP BW4HANA - Agile DWH

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

27PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Data Lake Concept

Data Lake Concept influencing SAP BW4HANA architectureThe Data Lake ndash single store of all enterprise data

A data lake is usually a single store of all enterprise

data including raw copies of source system data and

transformed data used for tasks such as reporting

visualization analytics and machine learning

EDW amp Data Marts Concept

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

28PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectureEnterprise analytics and the triad of Technology Architecture and Process

innovative

technology

adaptive

architecture

preserve

processes

innovative

technology

disruptive

architecture

new

processes

SAP BW4HANA

amp

SAP HANA

Technology

Process

Architecture

vision and industry

Agile -DWH

Data Lake

EDW-

simplified

flexible

Increasingly sophisticated and demanding customers are driving businesses

to evolve agile big-data-friendly data warehousing and analytics environments

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

SAP BW4HANA architectures

Agile DWH architecture with LSA++

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

30PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architecturesAgile DWH architecture with LSA++

Technology

Process

Architecture

Sufficient

Design Upfront

LSA++

SAP HANA

SAP BW4HANA

ODP

Incremental

governance

Virtual

Data Marts

DTO

Bottom-up

Field-

modeling

Open

ODS views

LDW

Mixed Scenarios

incremental

InfoObjects

Data Store

ObjectCorporate

Memory 20

Corporate Memory 20 and Open ODS Layer are low cost layers

supporting agile development processes on department project level

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Staging Layer

Corporate Memory 20

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

31PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key idea empower business more and more creating

solutions ndash change processes to an iterative and

incremental proceeding saying good-bye to monolithic IT

governance ndash incremental solutions amp governance

Agile SAP BW4HANA as cheap reliable platform for

integrating field-level data in a single location either

minus 11 in the Corporate Memory 20 and or

minus Transformed in the Open ODS Layer (RAW DWH

Layer)

Mixed scenarios (SAP HANA Calculation Views) on

advanced DSOs of the Corporate Memory 20 or the

Open ODS Layer play an important role

Capitalize on SAP BW4HANA authorization- query-

OLAP- lifecycle (DTO-) transport- hellip - services

SAP BW4HANA architecturesAgile DWH architecture with LSA++

go

vern

an

ce

Light

Strong

bo

tto

m u

p m

od

elin

g

Service Level

top

do

wn

mo

de

lin

g

Virtualization Virtual Data Marts

Architected

Data Mart

Source

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Staging Layer

Corporate Memory 20

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

32PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA Simplicity and Openness SAP BW4HANA modeling patterns for agile data warehousing

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

33PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

aDSOs

SAP BW4HANA ndash low cost fast data provisioning

Corporate Memory 20 as part of a Relational Data Lake

Calc ViewsaDSOs

tables

HANA

data marts

View-level

lsquoExtractionrsquoTable-level

BW4HANA semantic layer

Queries Open ODS Views CompositeProvider InfoObjects

Remote

Federation

Calc Views

DataSources

Analytics

DWH

layers

Relational Data Lake

Provisioning

Observations

bull Cover agile sql analytics requirements

bull Reconcile agile and real-time analytics

with DWHing

bull Different emphasis of low-cost table-

level provisioning

bull HANA (real time) data marts

bull DWH-persisted Layers

bull Overcome organization authorization

impact with analytics on source systems

bull New type of mix scenarios

bull BW4 as service provider for HANA

real time data marts

bull History services

bull NLS service

bull New challenges eg delta Business

Content

bull Table-level lsquoreplicationrsquo and view-level

extraction ndash no neither nor

BW4HANA

RAW DWH

Open ODS Layer

BW4HANA

Integrated DWH

Propagation Layer

aDSO aDSO

Corporate Memory 20

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

34PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer example

Nativ

e Han

a

Acquisition layer

Source

SAP BW

real world

Reporting

SAP BW

Scenario 2

Staging SnapshotScenario 1

Reporting Virtual

Business rules

Tech integration

Cleansing layer (optional)

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

SAP BW4HANA architectures Relational Data Lake architecture capitalizing

on SAP BW4HANA DWH-services

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

36PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Key ideas

A relational data lake or a Corporate Memory 20 is a single place for structured and semi-structured data of the entire enterprise on SAP HANA (SAP BW4HANA) with cheap fast data provisioning

Minimize DWH governance overhead amp capitalize on SAP BW4HANA services

Teams from departments and IT build incrementally virtual solutions on the relational data lake Corporate Memory 20 using SAP HANA calculation views SAP BW4HANA Open ODS Views SAP BW4HANA CompositeProvider SAP BW4HANA Queries

Semi-structured data (JSON) are stored in the SAP Docstore and immediately integrated with structured data eg via Open ODS Views

SAP BW4HANA offers solution independent services like authorization- transport- DTO- Olap- query-services

SAP BW4HANA offers modeling services like currency conversion time-handling value and semantical integrationhellip

Additional SAP BW4HANA DWH persisted data are driven by solution requirements (snapshots history stability)

SAP BW4HANA architectures Integrating Data Lake concepts Relational Data Lake architecture with SAP BW4HANA DWH-services

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Relational Data Lake

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

37PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANASources ndash Provisioning ndash Relational Data Lake

Business Processes

HSC Hydrocarbon Supply Chain

HTR Hire to Retire

OTC Order to Cash

PFR Plan for Reliability

PTP Procurement to Pay

RTR Record to Report

Initial Sources amp provisioning

Initial Content

Database Tables

Advanced DSOs

Relational

Data Lake

Ingestion

Layer

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

38PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Customer case ndash a Relational Data Lake architecture capitalizing on SAP BW4HANARelational Data Lake and Virtual Solutions

Calculation Views

Open ODS Views

BW Object Count

Open ODS Views 793

InfoObjects ~50

Advanced DSO 9

Composite

Providers 99

BW Queries 253

Virtu

aliz

atio

n

Virtu

al D

ata

Marts

Composite Provider

Capitalizing on SAP BW4HANA ndash examples Semantic model richness master data key figuresKPIs query definition

OLAP features (eg exception aggregation)

Prepared to easily scale into DW scenarios

lsquoWe learned to appreciate InfoObjectsrsquo (stable fundament)

Common authorization transports business model patterns

Database Tables

Advanced DSOs

Relational Data Lake

Ingestion Layer

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

39PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA integrating native Relational Data Lake on HANA

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

Tables

SAP BW4HANACorporate

Memory 20

Semi-structured

data (JSON)structured

data

SAP HANA

SAP BW4HANA

DWH Stack

Open ODS Views CompositeProvider

SAP HANA Calculation Views

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

aDSO

aDSOSAP HANA

Relational Data Lake Collections

Relational Data Lake

Rick Greenwald Research Director

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

40PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA architectures in times of digital transformation

Technology

Process

Architecture

Virtualization Virtual Data Marts

Architected

Data Mart

Open ODS Layer

Raw DWH

Propagation Layer

Integrated DWH

Corporate

Memory 20

bo

tto

m u

p m

od

eli

ng

top

do

wn

mo

de

ling

SAP HANA

Data Lake

Relational Data Lake

SAP Data Hub

Agile Data Provisioning

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

41PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

Gartner Enterprise Data Warehouse and Data Lake Extension

Rick Greenwald Research Director

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

42PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

SAP BW4HANA

Community Product Pagehttpssapcombw4hana10

Home Pagehttpssapcombw4hana

Documentationhttpshelpsapcombw4hana10

Product Roadmaphttpssapcomroadmaps

Training amp Certificationhttpstrainingsapcom

Get Started

Free Learning openSAP Free Trial

Get Help

FAQ QampA Community Wiki Support Services Availability

Read amp Learn More

SAP Blogs Community Blogs Learning Journey

Transition from SAP BW

Blog SAP Activate Roadmap SAP Readiness Check

Related Solutions

SAP HANA SAP Data Hub SAP Analytics Cloud

Share amp Follow

BW4HANA

TheFutureofDW

Watch

Demos amp Tutorials

YouTube Channel

SAP HANA Academy

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

43PUBLICcopy 2019 SAP SE or an SAP affiliate company All rights reserved ǀ

httpssaphanacloudservicescomdata-warehouse-cloud

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

Contact information

juergenhauptsapcom

Thank you

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us

copy 2019 SAP SE or an SAP affiliate company All rights reserved

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of

SAP SE or an SAP affiliate company

The information contained herein may be changed without prior notice Some software products marketed by SAP SE and its

distributors contain proprietary software components of other software vendors National product specifications may vary

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only without representation or

warranty of any kind and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials

The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty

statements accompanying such products and services if any Nothing herein should be construed as constituting an additional

warranty

In particular SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or

any related presentation or to develop or release any functionality mentioned therein This document or any related presentation

and SAP SErsquos or its affiliated companiesrsquo strategy and possible future developments products andor platforms directions and

functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason

without notice The information in this document is not a commitment promise or legal obligation to deliver any material code or

functionality All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations Readers are cautioned not to place undue reliance on these forward-looking statements and they

should not be relied upon in making purchasing decisions

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries All other product and service names

mentioned are the trademarks of their respective companies

See wwwsapcomcopyright for additional trademark information and notices

wwwsapcomcontactsap

Follow us