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

Junk dimensions


A s we've already seen, Junk dimensions are built from groups of attributes that don't belong on any other dimension, generally columns from fact tables that represent flags or status indicators. When designing an Analysis Services solution, it can be quite tempting to turn each of these columns into their own dimension, having just one attribute, but from a manageability and usability point of view creating a single Junk dimension is preferable to cluttering up your cube with lots of rarely-used dimensions. Creating a Junk dimension can be important for query performance too. Typically, when creating a Junk dimension, we create a dimension table containing only the combinations of attribute values that actually exist in the fact table—usually a much smaller number of combinations than the theoretical maximum, because there are often dependencies between these attributes and knowing these combinations in advance can greatly improve the performance of MDX queries that display...