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

Limiting the query output


MDX supports multiple ways of limiting query results. You could use the WHERE clause, often referred to as slicer, since it limits the result set by specifying a data slice. You could also use the FILTER function to specify the criteria for members included on each axis and thereby derive a more focused result set.

While reviewing the results of queries, as shown in the previous section, you probably noticed that the result sets included some empty cells. As you might imagine, large cubes could include many empty cells, and such data may or may not be desirable in the query's output. You have a couple of options for limiting the output to only non-null (non-empty) values.

How to do it...

Let's get started with limiting the query output.

  1. Execute the following queries to limit the output to only the components product category. As mentioned earlier, we can refer to a hierarchy member by its name or by its key, so either of the following statements will return the same...