Front cover Multidimensional Analytics: Delivered with InfoSphere Warehouse Cubing Services Getting more information from your data warehousing environment Multidimensional analytics for improved decision making Efficient decisions with no copy analytics Chuck Ballard Silvio Ferrari Robert Frankus Sascha Laudien Andy Perkins Philip Wittann ibm.com/redbooks International Technical Support Organization Multidimensional Analytics: Delivered with InfoSphere Warehouse Cubing Services April 2009 SG24-7679-00 Note: Before using this information and the product it supports, read the information in “Notices” on page vii. First Edition (April 2009) This edition applies to IBM InfoSphere Warehouse Cubing Services, Version 9.5.2 and IBM Cognos Cubing Services 8.4. © Copyright International Business Machines Corporation 2009. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Notices . vii Trademarks . viii Preface . ix The team that wrote this book . x Become a published author . xiii Comments welcome. xiv Chapter 1. Introduction. 1 1.1 Multidimensional Business Intelligence: The Destination . 2 1.1.1 Dimensional model . 3 1.1.2 Providing OLAP data. 5 1.1.3 Consuming OLAP data . 7 1.1.4 Pulling it together . 8 1.2 Conclusion. 9 Chapter 2. A multidimensional infrastructure . 11 2.1 The need for multidimensional analysis . 12 2.1.1 Identifying uses for a cube . 13 2.1.2 Getting answers with no queries . 16 2.1.3 Components of a cube . 17 2.1.4 Selecting dimensions . 17 2.1.5 Why create a star-schema . 18 2.1.6 More help from InfoSphere Warehouse Cubing Services. 20 2.2 An architecture . 20 2.2.1 From data sources to data marts . 21 2.2.2 From data marts to accessing cubes . 30 2.2.3 Accessing cubes . 34 2.2.4 Cubing Services ports . 36 2.3 Multidimensional life cycles . 37 2.3.1 Life cycle examples. 37 2.4 Designing for performance . 39 2.4.1 Small fact tables . 39 2.4.2 Logs . 39 2.4.3 Production statistics in development environment . 40 2.4.4 Limiting the optimization advisor . 40 2.5 Metadata . 42 2.6 Cubing services security . 43 2.6.1 Creating a role. 44 © Copyright IBM Corp. 2009. All rights reserved. iii 2.6.2 Configuring cube security . 48 Chapter 3. The relational multidimensional model . 55 3.1 Impact of cubing services on the relational model . 56 3.1.1 Dimensional modeling best practices . 56 3.1.2 Cubing services optimization advisor . 64 3.1.3 DB2 Design Advisor . 65 3.2 Topologies: star, snowflake, and multi-star schemas . 78 3.3 Model considerations . 80 3.3.1 Granularity. 80 3.3.2 Accessing multiple fact tables . 81 3.3.3 Join scenarios . 81 3.4 Deciding to use Relational DB, OLAP, or tooling . 84 3.4.1 RDBMS . 84 3.4.2 OLAP. 86 3.4.3 Tooling . 88 Chapter 4. Cubing services model implementation. 89 4.1 Dimensions . 90 4.1.1 Star and snowflake schema-based dimensions . 90 4.1.2 Time dimension type . 91 4.1.3 Balanced standard hierarchies . 94 4.1.4 Unbalanced standard hierarchies . 94 4.1.5 Ragged standard hierarchies . 96 4.1.6 Unbalanced recursive hierarchies. 98 4.1.7 Facts . 101 Chapter 5. Reporting solutions . 107 5.1 Query styles . 108 5.2 Ad-hoc query . 108 5.3 Analysis . 116 5.3.1 Exceptions. 119 5.3.2 Correlation. 120 5.3.3 Charting. 123 5.4 Authored reporting. 124 5.4.1 Layout and formatting . 125 5.4.2 Multiple queries . 127 5.4.3 Calculations. 129 5.4.4 Prompting . 130 5.4.5 Bursting . 132 5.5 Design approaches for performance . 133 5.5.1 Crossjoins . 133 5.5.2 Filtering . 134 5.5.3 Local processing . 139 iv Multidimensional Analytics: Delivered with InfoSphere Warehouse Cubing Services 5.5.4 Ad-hoc queries . 141 5.6 Common calculations . 141 5.6.1 Syntax changes. 141 5.6.2 Aggregates . 142 5.6.3 Relative time . 145 5.6.4 Solve order . 151 5.7 Building financial reports . ..