Business Intelligence Features

29.13 Business Intelligence Features

DB2 Data Warehouse Edition is an offering in the DB2 family that incorporates busi- ness intelligence features. Data Warehouse Edition has at its foundation the DB2 engine, and enhances it with features for ETL , OLAP , mining, and online reporting. The DB2 engine provides scalability using its MPP features. In the MPP mode, DB2

can support configurations that can scale to several hundreds of nodes for large database sizes (terabytes). Additionally, features such as MDC and MQT provide support for the complex query-processing requirements of business intelligence.

Another aspect of business intelligence is online analytical processing or OLAP . The DB2 family includes a feature called cube views that provides a mech- anism to construct appropriate data structures and MQT s inside DB2 that can be used for relational OLAP processing. Cube views provide modeling support for multidimensional cubes and provides a mapping mechanism to a relational star schema. This model is then used to recommend appropriate MQT s, indices, and MDC definitions to improve the performance of OLAP queries against the database. In addition, cube views can take advantage of DB2 ’s native support for the cube by and rollup operations for generating aggregated cubes. Cube views is a tool

1222 Chapter 29 IBM DB2 Universal Database

that can be used to integrate DB2 tightly with OLAP vendors such as Business Objects, Microstrategy, and Cognos.

In addition, DB2 also provides multidimensional OLAP support using the DB2 OLAP server. The DB2 OLAP server can create a multidimensional data mart from an underlying DB2 database for analysis by OLAP techniques. The OLAP engine from the Essbase product is used in the DB2 OLAP server.

DB2 Alphablox is a new feature that provides online, interactive, reporting, and analysis capabilities. A very attractive feature of the Alphablox feature is the ability to construct new Web-based analysis forms rapidly, using a building block approach called blox.

For deep analytics, DB2 Intelligent Miner provides various components for modeling, scoring, and visualizing data. Mining enables users to perform classi-

fication, prediction, clustering, segmentation, and association against large data sets.

Bibliographical Notes

The origin of DB2 can be traced back to the System R project (Chamberlin et al. [1981]). IBM Research contributions include areas such as transaction processing (write-ahead logging and ARIES recovery algorithms) (Mohan et al. [1992]), query processing and optimization (Starburst) (Haas et al. [1990]), parallel processing ( DB2 Parallel Edition) (Baru et al. [1995]), active database support (constraints, triggers) (Cochrane et al. [1996]), advanced query and warehousing techniques such as materialized views (Zaharioudakis et al. [2000], Lehner et al. [2000]), mul- tidimensional clustering (Padmanabhan et al. [2003], Bhattacharjee et al. [2003]), autonomic features (Zilio et al. [2004]), and object-relational support ( ADT s, UDF s) (Carey et al. [1999]). Multiprocessor query-processing details can be found in Baru et al. [1995]. Don Chamberlin’s books provide a good review of the SQL and pro- gramming features of earlier versions of DB2 (Chamberlin [1996], Chamberlin [1998]). Earlier books by C. J. Date and others provide a good review of the features of DB2 Universal Database for OS/390 (Date [1989], Martin et al. [1989]).

The DB2 manuals provide the definitive view of each version of DB2 . Most of these manuals are available online (http://www.software.ibm.com/db2). Books on DB2 for developers and administrators include Gunning [2008], Zikopoulos et al. [2004], Zikopoulos et al. [2007] and Zikopoulos et al. [2009].