This document discusses SAS In-Database, which allows SAS functions, models, and code to run directly inside databases. Key points include: - SAS In-Database aims to streamline analytics workflows, improve performance, and ensure data consistency. - It works by embedding SAS capabilities like scoring functions, modeling, and data preparation directly into databases. - SAS has partnered with Aster Data to enable these in-database analytics using Aster's nCluster platform.

1. SAS In-Database
Michelle Wilkie, Product Manager
Copyright © 2009, SAS Institute Inc. All rights reserved.

2. Agenda
 Overview of SAS
 SAS product and solutions
 SAS In-Database overview
 SAS and Aster Data partnership
Copyright © 2009, SAS Institute Inc. All rights reserved.

3. 2009 Worldwide Revenue
$2.31 Billion
Copyright © 2009, SAS Institute Inc. All rights reserved.

4. Customers
45,000 sites world-wide
1,389 customers added in 2009
Copyright © 2009, SAS Institute Inc. All rights reserved.

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

6. SAS® for Federal Government
Federal Financials Operations
Fraud & Improper Payments Disaster Preparedness/Emergency Response
Financial Risk Cybersecurity
Audit & Compliance Logistics
Financial Visibility Green IT/Sustainability
Budget & Performance Integration Data Center Optimization
Cost Management Operational Risk
Human Capital Organization
Workforce Planning & Analysis Performance Management
Recruitment & Retention IT Management
Healthcare Reporting
Copyright © 2009, SAS Institute Inc. All rights reserved.

7. SAS® for Banking
Risk Customers
Firmwide Risk Customer Experience Analytics
Credit Risk/Counterparty Risk Customer Profitability & Relationship Pricing
Market Risk Acquisition, On-Boarding & Retention
Asset/Liability Management Cross-Sell & Up-Sell
Operational Risk Collections Optimization
Fraud/Financial Crimes Marketing Optimization
Finance Operations
Regulatory Compliance Performance Measurement & Reporting
Capital Allocation & Management Workforce Planning & Management
Legal/Financial Consolidation & Reporting IT Performance Management
Cost & Profitability Management Sustainability/Green Initiatives
Copyright © 2009, SAS Institute Inc. All rights reserved.

8. What is SAS® In-Database?
Integration In-Database
SAS Applications are integrated to The ability to embed and use
leverage standard database SAS functions, framework,
features. processes and applications
inside the database.
Examples
Examples
• SAS Format function
• Database Specific SQL
• SAS Scoring functions
• SQL functions
• Predictive Modeling Functions
• Stored Procedures
Copyright © 2009, SAS Institute Inc. All rights reserved.

9. Value Proposition
SAS® In-Database
Capability Value
Streamline Analytic • Minimize data preparation
Workflow • Accelerate data discovery
• Decrease time to value
Scalability and • Reduce data movement
Performance • 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
Copyright © 2009, SAS Institute Inc. All rights reserved.

10. SAS® In-Database Overview
Traditional Architecture In-Database Architecture
Analytic Modeling Analytic Modeling
SAS Data
Data Preparation
Preparation Scoring
SAS SAS
Modeling Scoring
SAS C & Data
PMML Scoring Preparation
Data Warehouse / Database Data Warehouse / Database
Copyright © 2006, SAS Institute Inc. All rights reserved. Company confidential - for internal use only

11. SAS In-Database Direction
Short-term
To streamline and optimize the customers’ business
process
Data Data
Analytics Reporting
Preparation Exploration
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.
Copyright © 2009, SAS Institute Inc. All rights reserved.
11

12. Design Principles
SAS® In-Database
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 • SAS Language skills
experience • SAS Procedures experience
• SAS Environment knowledge
Maintain SAS • Scalability in Rows and Columns
Standards • Numerical accuracy and precision
• Statistical integrity
• Software quality
Copyright © 2009, SAS Institute Inc. All rights reserved.

13. +
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14. In-Database Scoring select * from
sas_score(
on mytable
sas_code(’hmeq.sas')
format_xml(’fmt.xml')
);
Aster nCluster
Queen
Publishing
Agent
Worker Worker Worker
SAS Schema
14 Copyright © 2009 Aster Data Systems and SAS Institute Inc. All rights reserved.

15. In-Database DATA Step Example
Pivoting transactional data into a time-series format
data aster.out;
keep item_num item_desc
ID
ID
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;
select * from m = month(datepart(processed_dttm));
sas_data_step( month_qty[m] + item_qty;
on retail_trans
partition by item_num if last.item_num then
sas_code('pivot.sas') output;
); run;
15 Copyright © 2009 Aster Data Systems and SAS Institute Inc. All rights reserved.

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