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

Dimension/measure group relationships


So far we've seen dimensions either having no relationship with a measure group or having a regular relationship, but that's not the whole story: there are many different types of relationships that a dimension can have with a measure group. Here's the complete list:

  • No relationship

  • Regular

  • Fact

  • Referenced

  • Many-to-Many

  • Data Mining

We'll discuss many-to-many relationships in the next chapter because they are an important topic on their own, but before that let's talk briefly about the relationship types we've not seen so far.

Fact relationships

Fact or degenerate dimensions are, as we saw in Chapter 1, dimensions that are built directly from columns in a fact table, not from a separate dimension table. From an Analysis Services dimension point of view, they are no different from any other kind of dimension, except that there is a special fact relationship type that a dimension can have with a measure group. There are in fact very few differences between a fact...