How to decrease the database size with automated housekeeping

©  2014 DataVard # 1

Joseph W. O‘Leary

DataVard, Inc.

Shrink your database with automated housekeeping


§  Reduce system size in regards to Logs and Temporary Data substantially

§  Improve loading performance §  Be better prepared for future

challenges like migration to SAP HANA® platform

§  Transparency §  Reduce data cleansing effort

§  Minimum effort, maximum benefit §  ERNA functionalities (i.e. cockpit

for more transparency) §  Experience of DataVard

consultants §  Price

§  Result of Analysis (DataVard BW Fitness Test): 1.308 GB temporary data and logs blocked nearly half of the system size 25 % database size reduction

§  Deletion of 750 GB unnecessary data (PSA, ChangeLog, Cube compression, RFC Logs)

§  Reduction of workload: only §  1 workday per month for

housekeeping

Customer Example

Customer Profile An international leader in specialist publishing in the area Science, Technology, Medicine.

“With the help of ERNA we were able to substantially reduce the effort for data cleansing, with minimum effort and realizing savings of 25 % of the system’s size. We are now well prepared for future challenges like SAP HANA®.”

Key figures Objectives Highlights

Database Size Reduction: 25% through Housekeeping


Clean up your system Size reduction example (Housekeeping and NLS)

183 183

998 321

918

780.3

650

325

312

156

0

48.1

0

500

1000

1500

2000

2500

3000

3500

Heute mit OutBoard und ERNA

OutBoard

Cube data

ODS data

Other data

Temporary data

Master data

Before After

-68%

-15%

-50%

-50%

Total DB space saved of 43%!


Use

r ha

ppin

ess

TCO

& d

ata

acce

ss

TCO

Smart Data Management

§  Performance optimization, Tuning

§  In-memory §  Ensure SLAs are met

GOALS TACTICS

§  Use appropriate storage: Archiving, NLS, Smart data access

§  Set up central policies

§  Define policies §  Set up housekeeping §  Automation

Information “at your fingertips”

speed and high availability is key.

Keep & store, but reduce costs.

Purge, delete, housekeeping

Hot Data Business critical data Data required for reporting and planning

Cold Data / Old Data Aged data, history Infrequent, rare use Need to keep (legal, internal, industry requirements)

Dead Data Technical data (e.g. logs, protocols, PSA) Redundant data


5%

15%

15%

9%

11%

32%

5% 5% 3%

Master data

Temporary data

Other data

PSA data

Changelog data

ODS data

Cube E data

Cube F data

Cube D data

Step 1: Fitness Test Typical distribution of data in a BW system

Comments: § Data you report on is

only 13-17% of the system size

§  Temporary data is subject to housekeeping (BALDAT, RS*DONE, ...)

§ Use the HANA sizing

report as a 1st indication (OSS note 1736976).

§ Create a plan for the

data lifecycle (data load to data exit)

“Only 12% of all data in BW is actually used” Source: Forrester research


Housekeeping activities

n  Application log n  Batch log n  IDoc tables (EDI40, EDIDS) n  qRFC, tRFC n  Job-Tables (TBTCO, TBTCP etc.) n  Change & Transportsystem n  Spool data (TST03) n  Table Change Protocols n  Batch Input Folders n  Alert Management Data (SALRT*) n  Old short dumps n  Batch input data

Netweaver

Scope of Housekeeping

n  Unused customers n  Unused vendors n  Phantom change documents n  Phantom texts

ERP

n  PSAs & Change Logs n  Request logs & tables (RSMON*

and RS*DONE) n  Unused dimension entries n  Unused master data n  Cube & Aggregate compression n  Temporary database objects n  NRIV buffering n  Table buffering n  BI-Statistics n  Process Chain Log n  Errorlogs n  Unused Queries n  Empty partitions n  BI Background processes n  Bookmarks n  Web templates

Business Warehouse


7 reasons housekeeping is not done

Organizational Functional

n  Unclear responsibilities Basis or Application-Team?

n  Low visibility What happens when its not done is not transparent

n  No dedicated resources Will management pay for cleanup?

n  Tools spread out in the system Many small programs that can be found in many SAP notes

n  Not enough functionality If there is a program it often only allows keep or delete, no archiving

n  Too high risk Deleting permanently is risky

n  No transparency no tool that says if cleanup is running correct and in full


Central cockpit for all activities


Recycle Bin makes more aggressive deletion possible

Ease discussions about retention times.

Instead of deleting data, you can temporarily store it in a compressed recycle bin, from which it can be deleted at a later date.

©  2014 DataVard # 10

Recycle Bin compression (example PSA)

Same retention time using less space

Today

6 m

onth

s P

SA

14 d

ays

PS

A

15 days- 6months compressed in recycle bin (Quick Restore possible)

Benefit:

ERNA Recycle Bin

Automated deletion

©  2014 DataVard # 11

Less work through one central access

Keep your whole SAP Landscape in good condition

ERNA runs either stand-alone in a single SAP System or is installed on a central instance to automatically housekeep the full system landscape

©  2014 DataVard # 12

Mass processing leads to lower implementation effort

By grouping objects, that are to be house kept, the implementation effort is kept low.

New objects can be automatically included to existing groups, by using wildcards and dynamic patterns.

©  2014 DataVard # 13

Calendar & Scheduler

Ensure, that the planned housekeeping activities are really being implemented.

There are multiple monitoring tools: the calendar view shows all planned tasks within the full SAP Landscape, where ERNA is running.

©  2014 DataVard # 14

Clean up your system in 3 steps

Start Run Deploy 1 32

§  Prepare Project

§  Kick off workshop

§  Analyze potential

§  Scope definition based on BW Fitness Test and archiving roadmap

§  Target storage definition

§  OutBoard™ (NLS / ERNA) installation

§  Initial customizing

§  Housekeeping setup

§  Object adjustments

§  Deletion and retrieval tests

§  Performance tests

§  Setup of ongoing archiving / deletion

§  Knowledge Transfer

§  Initial Housekeeping

§  Result validation

§  Optional: Tablespace reorganization

§  Project Sign-off & Support

§  Ongoing Housekeeping done by customer

©  2014 DataVard # 15

Your presenter:

Joseph W. O‘Leary Product Manager ILM DataVard, Inc.

[email protected]

Request a demo here: www.erna.datavard.com

©  2014 DataVard # 16

Who is DataVard

§  Specialized in Data Management for SAP

§  Customers range from SMEs (60 users) to Fortune 500 (e.g. Allianz, BASF, KPMG, Roche, Nestle)

§  Focus on Data Management and ABAP development

§  SAP and ABAP only §  SAP certified solutions for BW Nearline storage

and housekeeping §  Partnership with SAP in consulting (e.g. SLO) §  Partnership with SAP in development (e.g. ILM)

Success

Experience

Focus

©  2014 DataVard # 17

Copyright DataVard Inc. All rights reserved.

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