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

Many-to-many dimension relationships


In the dimensional model, the fact table has a many-to-one relationship with each dimension. However, sometimes this kind of modeling cannot represent the real world: for example, a product might belong to several categories. One way of solving this problem might be to choose a "primary" category for each product, to allow the use of a classical star schema. But, doing this, we lose possibly important information.

Analysis Services 2005 introduced the ability to handle many-to-many relationships between dimensions. This feature brings to the OLAP world the approach of modeling many-to-many relationships using bridge tables or factless fact tables that we saw in Chapter 2.

Implementing a many-to-many dimension relationship

Our example scenario for implementing a many-to-many relationship is based on Sales Reason. In Adventure Works, each internet sale has a list of reasons for the transaction. This list is the result of the customer being asked a multiple...