SAS aster data big data dc presentation public

Copyright © 2009, SAS Institute Inc. All rights reserved.

SAS In-DatabaseMichelle Wilkie, Product Manager


Agenda

Overview of SAS

SAS product and solutions

SAS In-Database overview

SAS and Aster Data partnership


2009 Worldwide Revenue$2.31 Billion


Customers45,000 sites world-wide 1,389 customers added in 2009



Federal Financials Operations

IT Management

Performance Management

Reporting

Operational Risk

Recruitment & Retention

Workforce Planning & Analysis

Healthcare

Cost Management

Data Center Optimization

Green IT/Sustainability

Logistics

Cybersecurity

Disaster Preparedness/Emergency Response

Budget & Performance Integration

Financial Visibility

Audit & Compliance

Financial Risk

Fraud & Improper Payments

Human Capital Organization

SAS® for Federal Government


Risk Customers

Workforce Planning & Management

Performance Measurement & Reporting

IT Performance Management

Sustainability/Green Initiatives

Marketing Optimization

Capital Allocation & Management

Regulatory Compliance

Legal/Financial Consolidation & Reporting

Cost & Profitability Management

Fraud/Financial Crimes

Collections Optimization

Cross-Sell & Up-Sell

Acquisition, On-Boarding & Retention

Customer Profitability & Relationship Pricing

Customer Experience Analytics

Operational Risk

Asset/Liability Management

Market Risk

Credit Risk/Counterparty Risk

Firmwide Risk

Finance Operations

SAS® for Banking


What is SAS® In-Database?

Integration In-Database

SAS Applications are integrated to

leverage standard database

features.

The ability to embed and use

SAS functions, framework,

processes and applications

inside the database.

Examples

• Database Specific SQL

• SQL functions

• Stored Procedures

Examples

• SAS Format function

• SAS Scoring functions

• Predictive Modeling Functions


Value Proposition

Capability Value

Streamline Analytic

Workflow

• Minimize data preparation

• Accelerate data discovery

• Decrease time to value

Scalability and

Performance

• Reduce data movement

• Leverage MPP systems for parallelization

Data Consistency • Reduce Data Redundancy

• Reduce Information Latency

Fit for IT • Enable Data Governance

• Increase Hardware Utilization

• Integrate with Resource Management

• Facilitate standardization on a single

enterprise analytics platform

SAS® In-Database

Copyright © 2006, SAS Institute Inc. All rights reserved. Company confidential - for internal use only

SAS® In-Database Overview

Traditional Architecture In-Database Architecture

Data Warehouse / Database

DataPreparation

Analytic Modeling

SAS Scoring

Data Warehouse / Database

Analytic Modeling

SAS Scoring

SAS Modeling

Data Preparation

SAS C & PMML Scoring

DataPreparation


SAS In-Database Direction

Short-term

To streamline and optimize the customers’ business process

11

Long-term

A database will be the next HOST in which SAS can

be deployed.

Allowing SAS to leverage the high performance

compute architecture and database features

seamlessly.

Data Preparation

Data Exploration

Analytics Reporting


Design Principles

Principle

Reduce Data Movement • Push data-intensive work to database

• Make use of database resources: disks and

CPUs

• Generate optimized SQL

• Re-use SAS C code libraries when needed

Preserve SAS user

experience

• SAS Language skills

• SAS Procedures experience

• SAS Environment knowledge

Maintain SAS

Standards

• Scalability in Rows and Columns

• Numerical accuracy and precision

• Statistical integrity

• Software quality

SAS® In-Database


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Copyright © 2009 Aster Data Systems and SAS Institute Inc. All rights reserved.14

In-Database Scoring

Publishing

Agent

Queen

SAS Schema

WorkerWorkerWorker

Aster nCluster

select * from

sas_score(

on mytable

sas_code(’hmeq.sas')

format_xml(’fmt.xml')

);

Copyright © 2009 Aster Data Systems and SAS Institute Inc. All rights reserved.15

In-Database DATA Step ExamplePivoting transactional data into a time-series format

ID

ID

data aster.out;

keep item_num item_desc

jan feb mar ...;

array month_qty[12] jan feb mar ...;

set aster.in;

by item_num;

if first.item_num then

do i = 1 to 12;

month_qty[i] = .;

end;

m = month(datepart(processed_dttm));

month_qty[m] + item_qty;

if last.item_num then

output;

run;

select * from

sas_data_step(

on retail_trans

partition by item_num

sas_code('pivot.sas')

);

Copyright © 2009, SAS Institute Inc. All rights reserved.Copyright © 2009, SAS Institute Inc. All rights reserved.

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SAS aster data big data dc presentation public