Book Image

Expert Cube Development with Microsoft SQL Server 2008 Analysis Services

Book Image

Expert Cube Development with Microsoft SQL Server 2008 Analysis Services

Overview of this book

Microsoft's SQL Server Analysis Services 2008 is an OLAP server that allows users to analyze business data quickly and easily. However, designing cubes in Analysis Services can be a complex task: it's all too easy to make mistakes early on in development that lead to serious problems when the cube is in production. Learning the best practices for cube design before you start your project will help you avoid these problems and ensure that your project is a success. This book offers practical advice on how to go about designing and building fast, scalable, and maintainable cubes that will meet your users' requirements and help make your Business Intelligence project a success. This book gives readers insight into the best practices for designing and building Microsoft Analysis Services 2008 cubes. It also provides details about server architecture, performance tuning, security, and administration of an Analysis Services solution. In this book, you will learn how to design and implement Analysis Services cubes. Starting from designing a data mart for Analysis Services, through the creation of dimensions and measure groups, to putting the cube into production, we'll explore the whole of the development lifecycle. This book is an invaluable guide for anyone who is planning to use Microsoft Analysis Services 2008 in a Business Intelligence project.
Table of Contents (17 chapters)
Expert Cube Development with Microsoft SQL Server 2008 Analysis Services
Credits
About the Authors
About the Reviewers
Preface
Index

Grouping and Banding


Often, we'll need to create groups of some kind on a dimension either to group long lists of members up into more user-friendly groups, or to group numeric attributes such as Age or measure values into bands or ranges. Analysis Services offers some functionality to help us do this. But as usual, we'll get much more flexibility if we design these groups into the dimension ourselves.

Grouping

First of all let's consider why we might want to group members on a large attribute hierarchy. Some dimensions are not only very large – there are a lot of rows in the dimension table – but they are also very flat, so they have very few attributes on them that are related to each other and have very few natural hierarchies. We might have a Customer dimension with millions of individual customers on it, and we might also have City and Country attributes, but even then it might be the case that for a large city, a user might drill down and see hundreds or thousands of customers. In this...