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    SQL Server Analysis Services 2012 Cube Development Cookbook

    Por Baya Dewald

    Sobre

    In Detail

    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.

    Approach

    A practical cookbook packed with recipes to

    help developers produce data cubes as quickly as possible by following step by step instructions, rather than explaining data mining concepts with

    SSAS.

    Who this book is for

    If you are a BI or ETL developer using SQL Server Analysis services to build OLAP cubes, this book is ideal for you.

    Prior knowledge of relational databases and experience with Excel as well as SQL development is required.

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