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2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

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 15

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

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

30. Demonstration Transparently Improving Performance of BI Solutions

31. 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

32. 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

33. 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

34. 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

35. Demonstration Enhancing BI Applications with Analytic Content

36. 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

37. For More Information Oracle.com Oracle Technology Network:
olap.html
Oracle Technology Network:
Product Discussion Forum: 37 37

38. Q
& A 38

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