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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

Merging partitions


Merging multiple partitions is common as the partitions age (become less frequently queried) or when you need to reduce the total number of database files. For example, in the year 2013, we might not be particularly interested in data from the 1990s but could expect historical data to be queried occasionally. If so, we could keep monthly partitions for the current year but merge partitions for each previous year into yearly partitions and perhaps even merge all of the 1990s data into a decade-level partition. If you work in an environment where data must be aggregated many times each day, you might create multiple partitions throughout the day and later merge all intraday partitions into a single daily partition. As you will learn in Chapter 8, Administering and Monitoring Analysis Services, synchronization speed is largely dependent on the number of files that must be transferred. Therefore, merging partitions could also optimize synchronization speed.

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