Book Image

SQL Server Analysis Services 2012 Cube Development Cookbook

Book Image

SQL Server Analysis Services 2012 Cube Development Cookbook

Overview of this book

Microsoft SQL Server is a relational database management system. As a database, it is a software product whose primary function is to store and retrieve data as requested by other software applications. SQL Server Analysis Services adds OLAP and data mining capabilities for SQL Server databases. OLAP (online analytical processing) is a technique for analyzing business data for effective business intelligence. This practical guide teaches you how to build business intelligence solutions using Microsoft’s core product – SQL Server Analysis Services. The book covers the traditional multi-dimensional model which has been around for over a decade as well as the tabular model introduced with SQL Server 2012. Starting with comparing MultiDimensional and tabular models – discussing the values and limitations of each, you will then cover the essential techniques for building dimensions and cubes. Following on from this, you will be introduced to more advanced topics, such as designing partitions and aggregations, implementing security, and synchronizing databases for solutions serving many users. The book also covers administrative material, such as database backups, server configuration options, and monitoring and tuning performance. We also provide a primer on MultiDimensional eXpressions (MDX) as well as Data Analysis expressions (DAX) languages. This book provides you with data cube development techniques, and also the ongoing monitoring and tuning for Analysis Services.
Table of Contents (19 chapters)
SQL Server Analysis Services 2012 Cube Development Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Defining perspectives


As mentioned earlier in this chapter, Analysis Services perspectives are similar to views defined in the relational database. Each multidimensional SSAS project could contain multiple cubes, several measure groups, hundreds of measures, many hierarchies, and thousands of attributes. Many cube users might only need a subset of these attributes to get their job done. Including unnecessary options in the reports and analytical views could simply confuse data consumers; instead, you should try to expose only the essential attributes to each user group by defining perspectives. Unlike relational views, perspectives should not be used for implementing security; they are simply intended to conceal the complexity of your solution.

The Adventure Works sample multidimensional database contains several perspectives, for example, the Direct Sales perspective exposes Internet Sales, Internet Orders, Internet Customers, and Exchange Rates measure groups, whereas the Finance perspective...