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

Calculation dimensions


All the calculations we have described so far have been calculated measures—they have resulted in a new member appearing on the Measures dimension to display the result of our calculation. Once created, these calculated measures can be used just like any other measure and even used in the definition of other calculated measures.

In some circumstances, however, calculated measures can be rather inflexible. One example of this is time series calculations. If we want to let our users see, for example, the year-to-date sum of the Sales Amount measure, we can use the technique explained earlier and create a Sales Amount YTD measure. It will be soon clear though that users will want to see the year-to-date sum not only for the Sales Amount measure but on many others. We can define a new calculated measure for each real measure where the YTD function might be useful but, doing so, we will soon add too many measures to our cube, making it harder for the user to find the measure...