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

Chapter 5. Adding Transactional Data such as Invoice Line and Sales Reason

In this chapter we will analyze how to add detailed information about each transaction in a fact table, such as invoice document and line number. We'll compare the use of MOLAP and ROLAP dimensions for this purpose, and we will use the drillthrough feature to expose this data to the end user. We will also explain the reason why this approach is better than exposing a large dimension directly to the end user.

In the second part of this chapter we will add to the sales cube a dimension that describes the reasons for a sale. Since each sale can have multiple reasons associated with it, we will make use of the many-to-many dimensions relationship feature of Analysis Services, discussing its properties and possible performance issues. We will also take a brief look at possible modeling patterns available using many-to-many dimensions relationships.