1. Oracle Data Warehouse Strategic Update Ray Roccaforte

2. Oracle World 2010 Oracle Data Warehouse Strategic Update Ray Roccaforte Vice President, Data Warehouse and Analytic Technology

3. The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

4. Four Pillars of Oracle’s DW Strategy Oracle as a Systems Provider: Database Machine Maximum Leverage of Massive Hardware Capabilities Embedded Analytics Oracle as a DW Solutions Provider: Exadata Intelligent Warehouse for Industries

5. Four Pillars of Oracle’s DW Strategy  Oracle as a Systems Provider: Database Machine Maximum Leverage of Massive Hardware Capabilities Embedded Analytics Oracle as a DW Solutions Provider: Exadata Intelligent Warehouse for Industries

6. 6 Sun Oracle Database Machine Database Machine eliminates the complexity of deploying database systems Balanced configuration Extreme performance and integrated analytics out of the box Deploys in days rather than months Pre-built, tested, standard, supportable configuration Includes Hardware Installation & Site Planning Software Configuration

7. Exadata Hardware Architecture Database Grid 8 compute servers (1U) Storage Grid 14 storage servers (2U) 100 TB High Speed disk, or 336 TB High Capacity disk 5 TB PCI Flash Data mirrored across storage servers Scaleable Grid of industry standard servers for Compute and Storage InfiniBand Network Redundant 40Gb/s switches Unified server & storage net © 2010 Oracle Corporation 7

8. New Exadata X2-8 Full Rack Database Machine for large deployments Consolidation, or Large OLTP and DW workloads Complements existing X2-2 2 8-socket Intel EX servers (Sun Fire X4800) Doubles Database CPU cores to 128 Quadruples Database Memory to 2TB Oracle Solaris or Oracle Enterprise Linux 10GB Ethernet connectivity to Data Center Same 14 storage servers, and InfiniBand Network N ew Intel 6-core CPUs in Storage Servers 8 © 2010 Oracle Corporation

9. Four Pillars of Oracle’s DW Strategy Oracle as a Systems Provider: Database Machine  Maximum Leverage of Massive Hardware Capabilities Embedded Analytics Oracle as a DW Solutions Provider: Exadata Intelligent Warehouse for Industries

10. 10 Exadata Hybrid Columnar Compression Data is grouped by column and then compressed Query Mode for data warehousing Optimized for speed 10X compression typical Scans improve proportionally Archival Mode for infrequently accessed data Optimized to reduce space 15X compression is typical Up to 50X for some data

11. 11 Sun Oracle Database Machine provides semiconductor cache hierarchy Sun Oracle Database Machine: Faster raw scans Exadata disks 100TB raw disk capacity 21 GB/sec Exadata Smart Flash Cache 5TB raw capacity – up to 50TB user data 50 GB/sec Database DRAM Cache 400GB raw capacity – up to 4TB user data 100 GB/sec

12. 12 In-Memory Parallel Execution Parallel query processing across tables which are cached in memory How it works: Oracle determines appropriate tables for caching Tables are distributed across the buffer cache in all nodes During queries, each node reads its local portion of the table Full parallel bandwidth accessing data in memory Not every database objects in a query must be in memory to leverage in-memory access 12

13. Exadata Flash Cache Exadata has 5 TB of Flash: 56 Flash Cards avoid disk controller bottlenecks Automatically caches frequently-accessed ‘hot’ data in flash storage Intelligently manages Flash: Gives speed of Flash at cost of disk Exadata Flash Cache achieves: Over 1 million IO/sec from SQL (8K), Sub- millisecond response times with 50 GB/sec query throughput Infrequently Used Data Frequently Used Data

14. 14 Scanning from Disk: Exadata Smart Scan Exadata storage servers implement data intensive processing in storage Row filtering based on “where” predicate Column filtering Join filtering Incremental backup filtering Scans on Hybrid Columnar Compressed data Scans on encrypted data Data Mining model scoring 10x reduction in data sent to DB servers is common No application changes needed Processing is automatic and transparent Even if cell or disk fails during a query

15. Four Pillars of Oracle’s DW Strategy Oracle as a Systems Provider: Database Machine Maximum Leverage of Massive Hardware Capabilities  Embedded Analytics Oracle as a DW Solutions Provider: Exadata Intelligent Warehouse for Industries

16. Oracle In-Database Analytics Analytics evaluated “close” to the data Integrated Secure Oracle Data Mining: Uncover & Predict Advanced Predictive Analytics Advanced Algorithms Embedded and Integrated Oracle OLAP: Analyze & Summarize Smarter, Faster BI Improved Developer Productivity Embedded and Integrated

17. Oracle Data Mining Sift through large amounts data to find hidden patterns, discover new insights, and make predictions Data Mining can provide valuable results: Predict customer behavior (Classification) Predict or estimate a value (Regression) Segment a population (Clustering) Identify factors more associated with a business problem (Attribute Importance) Find profiles of targeted people or items (Decision Trees) Determine important relationships and “market baskets” within the population (Associations) Find fraudulent or “rare events” (Anomaly Detection)

18. In-Database Data Mining Exadata Smart Scan Model scoring performed in the Exadata Storage Server Dramatic (2-5X) performance improvements SELECT cust_id from customers WHERE region = ‘US’ AND prediction_probability(churnmod, ‘Y’, using *) > 0.8;

19. Exadata Integration with SAS Score SAS Models in Exadata Import SAS models (via PMML) to Oracle Data Mining Logistic and Multiple Regression models available today High-performance scoring in Exadata Score directly against database tables Model scoring is executed in storage tier using native Oracle DM capabilities +

20. Oracle OLAP Rapid Development of rich dimensional analytics Enhances the analytic content of Business intelligence application Excellent query performance for ad-hoc / unpredictable query Fast, incremental updates of data sets Embedded in the Oracle database instance and storage. Safe, secure and manageable Benefits from and enhances Exadata architecture Automatically gets full benefit of Database Machine Flash Cache OLAP query I/O tends to be high volume, random reads

21. Oracle OLAP 11g Rich Calculations Delivered to SQL-Based BI Tools

22. OLAP MDX driver from Simba for MS Excel © 2009 Oracle Corporation MDX Parser MDX Provider Engine SQL Generator SQL Query of Cube Excel MDX Query MDX Provider for Oracle OLAP Excel 2010 Excel 2003

23. Four Pillars of Oracle’s DW Strategy Oracle as a Systems Provider: Database Machine Maximum Leverage of Massive Hardware Capabilities Embedded Analytics  Oracle as a DW Solutions Provider: Exadata Intelligent Warehouse for Industries

24. Intelligence –Industry-specific data models –Packaged advanced analytics... with Extreme Performance –Improve query performance 10-100x with Exadata … at a Lower Cost –Simplify your infrastructure … for Fast Results –Jumpstart development - deliver value quickly –Automatically exploit Oracle performance and analytic capabilities Exadata Intelligent Warehouse for Industries A Complete Data Warehouse Solution for Industries

25. Exadata Intelligent Warehouse Solution for Industries Packaged Experience and Technology Leverage enterprise-wide data model for industries Gain insights using prepackaged advanced analytics Leverage enterprise-wide data model for industries Gain insights using prepackaged advanced analytics Business Insight Improve query performance 10 -100x Grow solution to virtually any scale Improve query performance 10 -100x Grow solution to virtually any scale Extreme Performance Jumpstart development and deliver value quickly Lower risks Jumpstart development and deliver value quickly Lower risks Fast Time-to-Value

26. Exadata Intelligent Warehouse for Retail Packaged Experience and Technology Copyright © 2010, Oracle and / or its affiliates. All rights reserved. Improve query performance 10 -100x Grow solution to virtually any scale Improve query performance 10 -100x Grow solution to virtually any scale Extreme Performance Jumpstart development and deliver value quickly Lower risks Jumpstart development and deliver value quickly Lower risks Fast Results Leverage enterprise data model for retail Gain insights using prepackaged advanced analytics Leverage enterprise data model for retail Gain insights using prepackaged advanced analytics Business Insight Leverage enterprise data model for retail Gain insights using prepackaged advanced analytics Leverage enterprise data model for retail Gain insights using prepackaged advanced analytics Business Insight

27. Challenges We’re Hearing from Retailers Forecast demand and inventory levels for better in-stock positions Apply business intelligence behind every planning decision Reduce Stock-outs Differentiate store assortments by customer profile of store locations Unlock trends hidden in transaction data Provide the right products all the time Improve Margins With Localized Assortments Determine customer segments Perform Market Basket analysis Target promotions to enhance loyalty Increase Share of Wallet Track key metrics for efficient store management Optimize store staffing levels Monitor shelf availability Improve In-store Execution

28. Exadata Intelligent Warehouse for Retail Consolidated View of the Entire Business Oracle Retail Data Model POS Order Mgmt Workforce Scheduling Sell Side PO Mgmt Warehouse Pricing Costing Buy Side HR Finance Sales Forecasting Inside Partners Suppliers Distributors Outside 1200+ Industry-specific Measures & KPIs

29. Interactive Dashboards and Reports Empowering End Users Anticipate out-of-stock using statistical forecasts Example Analytics: Retail Associate products using a market basket analysis Analyze store traffic and shopper conversion rates by time of day Understand effectiveness of promotions by store Analyze comparative store performance over time 29 Copyright © 2010, Oracle and / or its affiliates. All rights reserved.

30. Exadata Intelligent Warehouse for Communications Same idea, but specific to Communications industry

31. Interactive Dashboards and Reports Empowering End Users Identify customers that are likely to churn Example Analyses: Communications Monitor dropped call rate over time to detect failing network elements Compare billed and actual CDRs to identify billing errors Analyze sales growth over time by product and organization 31 Copyright © 2010, Oracle and / or its affiliates. All rights reserved.

32. Exadata Intelligent Warehouse Packaged Experience and Technology Copyright © 2010, Oracle and / or its affiliates. All rights reserved. Improve query performance 10 -100x Grow solution to virtually any scale Improve query performance 10 -100x Grow solution to virtually any scale Extreme Performance Jumpstart development and deliver value quickly Lower risks Jumpstart development and deliver value quickly Lower risks Fast Results Leverage enterprise data model for Industries Gain insights using prepackaged advanced analytics Leverage enterprise data model for Industries Gain insights using prepackaged advanced analytics Business Insight

33. Exadata Intelligent Warehouse Packaged Experience and Technology Copyright © 2010, Oracle and / or its affiliates. All rights reserved. Improve query performance 10 -100x Grow solution to virtually any scale Improve query performance 10 -100x Grow solution to virtually any scale Extreme Performance Jumpstart development and deliver value quickly Lower risks Jumpstart development and deliver value quickly Lower risks Fast Results Leverage enterprise data model for Industries Gain insights using prepackaged advanced analytics Leverage enterprise data model for Industries Gain insights using prepackaged advanced analytics Business Insight

34. Custom Solutions Key Implementation Tasks Assemble Hardware Install Specialized Software Assemble & Configure System Requirements Collection ERD Specification Design Data Model Define tables, views, cubes, analytics Implement indexes, partitions Implement Data Model Ensure system is balanced Modify performance structures (MVs, indexes, etc.) Tune Performance Create BI metadata Implement reports and dashboards Define Metrics & Reports Challenges: Design a data model that satisfies existing and future requirements Employ a diverse skill set Integrate, implement, administer and tune disparate technologies

35. Exadata Intelligent Warehouse Best Practice Implementation ASSEMBLED Sun Oracle Database Machine DESIGNED Oracle Retail Data Model IMPLEMENTED Oracle Best Practice DW Methodology FAST Sun Oracle Database Machine READY Oracle OBIEE Complete. Fast Results. Lower Risk. Benefits: Utilize an enterprise data model for retail Leverage your existing Oracle expertise Implemented using Oracle data warehousing best practices

36. Fast Results Retail Performance Metrics and Insight out of the box Speed to Value, Simplified Deployment with predictable cost Build from Scratch with Best of Breed Approach Oracle Exadata Intelligent Warehouse weeks or monthsmonths or years Sizing and Configuration Define Metrics & Dashboards Training & Roll-out Data Integration Sizing and Configuration Analysis and Design Define Metrics & Dashboards Analysis and Design Training & Roll-out

37. Exadata Intelligent Warehouse Solution for Industries Packaged Experience and Technology Business Insight Extreme Performance Fast Time-to-Value

38. Summary: Oracle’s DW Strategy Oracle as a Systems Provider: Database Machine Maximum Leverage of Massive Hardware Capabilities Embedded Analytics Oracle as a DW Solutions Provider: Exadata Intelligent Warehouse for Industries