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

Managing partitions


As we saw in Chapter 8, partitioning a measure group simply involves slicing it up into smaller chunks that are both easier to maintain and to query. In that chapter we already introduced and explained the basic concepts of partitioning; now we are interested in how to manage partitions when the cube is up and running.

Measure groups are usually partitioned by the time dimension, for example with one partition holding one month of data. Although there are rare cases where we might want to partition a measure group based on a different dimension (for example, geography is sometimes used), the vast majority of projects we have worked on use time as the slicer. This follows from the fact that new data usually needs to be loaded into the measure group at regular intervals in time, and this new data needs to be processed and stored alongside the existing data.

Clearly, since partitioning is so closely linked to the concept of time, we need to be able to build and process new...