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Enhancing the Performance and Analytic Content of the Data Warehouse Using Oracle OLAP Option

Bud Endress, Director of Product Management - OLAPSeptember 5, 2008

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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.

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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

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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

OLAP in the Data Warehouse

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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

OLAP in the Data Warehouse

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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?

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• 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

Oracle OLAP Option

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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

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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

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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

• SQL

SELECT time, product, customer, sales_ytdFROM sales_cube

SELECT time, product, customer, sales_ytdFROM sales_cube

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OLAP Option

• A content rich solution• Rich aggregations

• Time series

• Indices and market shares

• Rankings

• Forecasting

• Allocations

• Statistics

• Calculations are embeddedin the database• Centrally managed for consistency

• Accessible by any application

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OLAP Option

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

• OLAP cubes are optimized for ad-hoc, exploratory usage patterns

Static Reporting Self Service Reportingand Analysis

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OLAP Option

• OLAP cubes offer excellent performance for unpredictable query patterns• Appropriate for both

static and exploratoryreporting

• Advantages increaseas reporting becomesmore exploratory

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OLAP Option

• OLAP Cubes offer fast, incremental updates of data sets• Manage all summaries in a

single database object

• Fast, incrementalmaterialized view refresh

• Incremental / fastaggregation

• Cost-basedaggregation

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OLAP Option

• OLAP Cubes offer fast, incremental updates of data sets• Manage all summaries in a

single database object

• Fast, incrementalmaterialized view refresh

• Incremental / fastaggregation

• Cost-basedaggregation

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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

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SQL Query of OLAP Cubes

BI Application

CubeMaterialized

Views

SQL

Automatic Query

Rewrite

BI Application

Cube Views

SQL

Oracle Cube

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One Cube, Dimensional or SQL ToolsSingle version of the truth

SQL Query

OLAP Query

MetadataData

Business Rules

Extract, Load & Transform (ELT)

Centrally managed data, meta data and business rules

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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

Summary Data: Collections of Materialized Views

Materialized ViewsTypical MV Architecture Today

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Fact Table: Sales by Day, Item, Customer and Channel

BI Application

BI Application

SELECT SUM(sales)GROUP BY quarter, brand,region, channel

Automatic Query

Rewrite

• 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

Cube-Organized Materialized ViewsAutomatic Query Rewrite

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Fact Table: Sales by Day, Item, Customer and Channel

BI Application

BI Application

SELECT SUM(sales)GROUP BY quarter, brand,region, channel

Automatic Query Rewrite

• 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

• 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

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Typical query issued by Oracle Business Intelligence Enterprise Edition.

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.

Query is automatically rewritten by Oracle to access summary data in the cube-organized materialized view.

Cube-Organized Materialized ViewsFast, Incremental MV Refresh

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Fact Table: Sales by Day, Item, Customer and Channel

BI Application

BI Application

SELECT SUM(sales)GROUP BY quarter, brand,region, channel

• 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

• 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

MV Refresh

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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

Cube Organized Materialized ViewsCase 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

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Cube Organized Materialized ViewsCase 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

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Cube Organized Materialized ViewsCase 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.

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Cube Organized Materialized ViewsCase Study Results

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Time in minutes to

OLAP Cubes ViewsSQL Query of Oracle Cubes

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OLAP Cube Includes

• All levels of summarization

• Rich analytical calculations

OLAP Cube Includes

• All levels of summarization

• Rich analytical calculations

Cube is represented as star schema of relational views

• Dimension and fact views

• Detail and summary fact rows

• Rich analytic fact columns

Cube is represented as star schema of relational views

• Dimension and fact views

• Detail and summary fact rows

• Rich analytic fact columns

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Empowering Any SQL-Based Tool Simple SQL Queries Advanced Cube Content

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;

Application Express on Oracle OLAP

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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.

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.

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SQL issued by Oracle BI EE against views of Oracle cube and dimensions.

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.

New Joined Cube Scan row source pushes joins into the cube and accesses summary data and calculations.

Oracle OLAP OptionSummary

• 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.

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For More Information

• Oracle.com• 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

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