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

Details about transactional data


The goal of a multidimensional cube is to analyze aggregated data across several dimensions. However, when there is some interesting data, the user might be interested in drilling down to a lower level of detail. For example, when it comes to sales analysis, it could be interesting to look at the individual invoices that caused a particular high volume of sales in a single month. This is a very common request for end users to make – in fact, the question is not if the users will need this, but when.

One approach to solve this issue is to add a regular dimension to the cube which has the same granularity as the fact table – as we saw in Chapter 2, this is referred to as a Fact dimension. Using columns on the fact table like invoice number, invoice line number, notes and so on, we can link each fact sale with a dimension member, calling the dimension itself something like "Document". At this point, the end users will have a dimension that can be used with other...