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

Scale-up and scale-out


Buying better or more hardware should be your last resort when trying to solve query performance problems: it's expensive and you need to be completely sure that it will indeed improve matters. Adding more memory will increase the space available for caching but nothing else; adding more or faster CPUs will lead to faster queries but you might be better off investing time in building more aggregations or tuning your MDX. Scaling up as much as your hardware budget allows is a good idea, but may have little impact on the performance of individual problem queries unless you badly under-specified your Analysis Services server in the first place.

If your query performance degenerates as the number of concurrent users running queries increases, consider scaling-out by implementing what's known as an OLAP farm. This architecture is widely used in large implementations and involves multiple Analysis Services instances on different servers, and using network load balancing to...