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Bud Endress, Director of Product Management - OLAP September 5, 2008 Enhancing the Performance and Analytic Content of the Data Warehouse Using Oracle OLAP Option Bud Endress, Director of Product Management - OLAP September 5, 2008

The following is intended to outline our general product direction 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. 3

OLAP in the Data Warehouse Use Oracle OLAP to enhance your data warehouse Simplified summary management ‘Speed of thought’ query performance Advanced time series analysis and analytic content Centralized management of data, meta data, calculations and security 4

OLAP in the Data Warehouse Every data warehouse can benefit from Oracle OLAP Every business intelligence tool accesses summary data Every business user wants excellent query performance in both static and exploratory BI applications Every business user will benefit from rich analytic content 5

OLAP in the Data Warehouse Embedded Oracle OLAP is preferred by IT to external solutions Use the database you already own Use the BI tools they already own Use Oracle skills you already have Embedded Oracle OLAP is secure and enterprise ready 6

OLAP in the Data Warehouse Ask yourself the following questions Do you use business intelligence tools? Oracle BI EE, Business Objects, Cognos, MicroStrategy, etc.? Would business users benefit from Significantly improved query performance? Rich analytic content? Would IT benefit from Fast, efficient updates of data sets? Fewer servers to manage? Consolidating stand alone OLAP servers into the database? 7

Oracle OLAP Option A summary management solution for SQL based business intelligence applications An alternative to table-based materialized views, offering improved query performance and fast, incremental update A full featured multidimensional OLAP server Excellent query performance for ad-hoc / unpredictable query Enhances the analytic content of Business intelligence application Fast, incremental updates of data sets 8

OLAP Option An embedded OLAP solution Runs within Oracle Database Enterprise Edition Data are stored in Oracle data files Meta data in the Oracle Data Dictionary Fully compatible with RAC / Grid computing 9

OLAP Option A secure solution Oracle users are OLAP users SQL GRANT / REVOKE on OLAP cubes and dimensions Compatible with Virtual Private Database Fine Grained Cube Security Oracle Authentication SQL Cube Access Control Virtual Private Database Fine Grained Cube Security 10

OLAP Option An open solution Oracle cubes and dimensions are queried using SQL PL / SQL Oracle OLAP API Transparent access as cube-organized materialized view SELECT time, product, customer, sales_ytd FROM sales_cube 11

OLAP Option A content rich solution Rich aggregations Time series Indices and market shares Rankings Forecasting Allocations Statistics Calculations are embedded in the database Centrally managed for consistency Accessible by any application 12

OLAP Option OLAP cubes are optimized for ad-hoc, exploratory usage patterns Predictable query environment Predefined reports Predefined calculations Less exploration of data Exploratory query environment Users define reports Users access any data Users define calculations More users amplify this effect Static Reporting Self Service Reporting and Analysis 13

OLAP Option OLAP cubes offer excellent performance for unpredictable query patterns Appropriate for both static and exploratory reporting Advantages increase as reporting becomes more exploratory 14

OLAP Option OLAP Cubes offer fast, incremental updates of data sets Manage all summaries in a single database object Fast, incremental materialized view refresh Incremental / fast aggregation Cost-based aggregation 15

OLAP Option OLAP Cubes offer fast, incremental updates of data sets Manage all summaries in a single database object Fast, incremental materialized view refresh Incremental / fast aggregation Cost-based aggregation 16

OLAP Option One cube can be used as A summary management solution to SQL-based business intelligence applications as cube-organized materialized views A analytically rich data source to SQL-based business intelligence applications as SQL cube-views A full-featured multidimensional cube, servicing dimensionally oriented business intelligence applications 17

Cube Materialized Views Automatic Query Rewrite SQL Query of OLAP Cubes BI Application Cube Materialized Views SQL Automatic Query Rewrite BI Application Cube Views SQL Querying OLAP cubes with SQL is easy. Aggregation and calculation rules are embedded and executed within the cube, the application just queries OLAP cube and dimension views by selecting measure columns from a fact view at the level of summarization required by the query. The innovative ‘embedded summary’ style fact view represents measures as columns and includes all detail and summary level data of the cube in a single view. Rather than having to express calculation rules in the query, the application just queries measure columns and requests the levels of summarization needed by the query. Rich content and summary data is made accessible with very simple SQL. Oracle Cube 18

One Cube, Dimensional or SQL Tools Single version of the truth Metadata Data Business Rules OLAP Query Extract, Load & Transform (ELT) SQL Query Centrally managed data, meta data and business rules 19

Cube Organized Materialized Views Transparently enhance the query performance of BI applications Data is managed in an Oracle cube Fast query Fast refresh Manage a single cube instead of 10’s, 100’s or 1,000’s of table-based materialized views Applications query base / detail relational tables Oracle automatically rewrites SQL queries to OLAP cubes Access to summary data in the cube is fully transparent 20

Materialized Views Typical MV Architecture Today BI Application Summary Data: Collections of Materialized Views Users expect excellent query response for all summary queries Might require 10’s, 100’s or even 1,000’s of materialized views Difficult to manage Longer build and update times Automatic Query Rewrite SELECT SUM(sales) GROUP BY quarter, brand, region, channel Fact Table: Sales by Day, Item, Customer and Channel 21

Cube-Organized Materialized Views Automatic Query Rewrite BI Application A single cube manages summaries for all groupings in the model A cube can be represented as a cube-organized materialized view Oracle automatically rewrites summary queries to the cube A singe cube can replace 10’s, 100’s or 1,000’s of materialized views SELECT SUM(sales) GROUP BY quarter, brand, region, channel Automatic Query Rewrite Fact Table: Sales by Day, Item, Customer and Channel 22

Typical query issued by Oracle Business Intelligence Enterprise Edition. Query is automatically rewritten by Oracle to access summary data in the cube-organized materialized view. 23

Cube-Organized Materialized Views Fast, Incremental MV Refresh BI Application A single cube is refreshed using MV refresh system Fast, incremental update from MV logs. Fast, incremental aggregation within the cube. Efficient management of sparse data sets. Replaces 10’s, 100’s or even 1,000’s of table-based MVs SELECT SUM(sales) GROUP BY quarter, brand, region, channel MV Refresh Fact Table: Sales by Day, Item, Customer and Channel 24

Cube Organized Materialized Views An excellent summary management solution for business intelligence tools such as BI EE, MicroStrategy, Cognos and Business Objects Cube organized materialized views are similar to materialized views on pre-built tables Cube organized materialized views are meta data only – they do not store data; data comes from the cube A common implementation will be to leave detail data in tables and create the cube at aggregate levels E.g. tables with day, customer and cube with month, zip code 25

Cube Organized Materialized Views Case Study Compares performance of table-based materialized views with cube-organized materialized views with goals of: Improving query performance of SQL-based BI tools Reducing build/update times Source data Fast moving consumer goods company data 7 dimensions 20 million fact rows 26

Cube Organized Materialized Views Case Study Methodology Indexes and materialized views were created as per Oracle SQL Access Advisor recommendations. 124 materialized views 198 indexes Oracle cube and cube-organized materialized views were created by DBA. 1 compressed cube Pre-aggregated to 20% 1469 test queries 27

Cube Organized Materialized Views Case Study Measurements Time to load data and prepare it for query MVs: Create MVs, create indexes and compute statistics Cube: Load data and aggregate. Query performance Run the same 1469 queries against MVs and cube. 28

Cube Organized Materialized Views Case Study Results Time in minutes to 29

Demonstration Transparently Improving Performance of BI Solutions

OLAP Cubes Views SQL Query of Oracle Cubes Cube is represented as star schema of relational views Dimension and fact views Detail and summary fact rows Rich analytic fact columns OLAP Cube Includes All levels of summarization Rich analytical calculations 31

Empowering Any SQL-Based Tool Simple SQL Queries Advanced Cube Content Application Express on Oracle OLAP SELECT cu.long_description customer, f.profit_rank_cust_sh_parent, f.profit_share_cust_sh_parent, f.profit_rank_cust_sh_level, f.profit, f.gross_margin FROM time_calendar_view t, product_primary_view p, customer_shipments_view cu, channel_primary_view ch, units_cube_view f WHERE t.level_name = 'CALENDAR_YEAR' AND t.calendar_year = 'CY2006' AND p.dim_key = 'TOTAL' AND cu.parent = 'TOTAL' AND ch.dim_key = 'TOTAL' AND t.dim_key = f.TIME AND p.dim_key = f.product AND cu.dim_key = f.customer AND ch.dim_key = f.channel; 32

Oracle cubes can make any BI tool smarter and faster. Oracle Business Intelligence Enterprise Edition querying time series calculations directly from an Oracle cube using SQL. Oracle cubes can make any BI tool smarter and faster. 33

SQL issued by Oracle BI EE against views of Oracle cube and dimensions. New Joined Cube Scan row source pushes joins into the cube and accesses summary data and calculations. 34

Demonstration Enhancing BI Applications with Analytic Content

Oracle OLAP Option Summary Enhances the performance and analytic content of SQL-based business intelligence applications. May be used as: A summary management solution with cube-organized materialized views. A full-featured multidimensional cube and calculation engine queried directly with SQL Embedded in the Oracle database instance and storage. Safe, secure and manageable. Fully compatible with Grid Computing/Real Application Clusters. 36

For More Information Oracle.com Oracle Technology Network: http://www.oracle.com/solutions/business_intelligence/ olap.html Oracle Technology Network: http://www.oracle.com/technology/products/bi/olap/index.html Product Discussion Forum: http://forums.oracle.com/forums/forum.jspa?forumID=16 37 37

Q & A 38

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