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  • Book Overview & Buying SQL Server Analysis Services 2012 Cube Development Cookbook
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SQL Server Analysis Services 2012 Cube Development Cookbook

SQL Server Analysis Services 2012 Cube Development Cookbook

By : Baya Dewald
4.5 (2)
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SQL Server Analysis Services 2012 Cube Development Cookbook

SQL Server Analysis Services 2012 Cube Development Cookbook

4.5 (2)
By: Baya Dewald

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 (14 chapters)
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13
Index

Creating a calculated measure


One of the great strengths in a model is the ability to create calculated measures and calculated columns. This recipe will cover calculated measures, and the next recipe will discuss calculated columns. However, this is a good place to differentiate between the two.

A good example of a calculated measure would be the sum of sales in our sample model. In this case, we will want to slice the sum by many different values within the model, such as date, customer, and product. A calculated measure will use these values to create a slice of references that are used to get the sum of sales.

One example of a calculated member is a formatted customer name such as Last name, first name. In this case, you need to use the context of the row in the table as part of the calculation because each row will use its values in the calculation. More on calculated columns can be found in the next recipe.

In this recipe, you will learn how to create calculated measures within the model...

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