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  • Book Overview & Buying Expert Cube Development with SSAS Multidimensional Models
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Expert Cube Development with SSAS Multidimensional Models

Expert Cube Development with SSAS Multidimensional Models

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Expert Cube Development with SSAS Multidimensional Models

Expert Cube Development with SSAS Multidimensional Models

4.5 (19)
Buy this Book

Overview of this book

An easy-to-follow guide full of hands on examples of real-world Analysis Services cube development tasks. Each topic is explained and placed in context, and for the more inquisitive reader, there also more in-depth details of the concepts used. If you are an Analysis Services cube designer wishing to learn more advanced topic and best practices for cube design, this book is for you.You are expected to have some prior experience with Analysis Services cube development.
Table of Contents (19 chapters)
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Expert Cube Development with SSAS Multidimensional Models
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Expert Cube Development with SSAS Multidimensional Models
Credits
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Credits
About the Authors
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About the Authors
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
Designing the Data Warehouse for Analysis Services
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Designing the Data Warehouse for Analysis Services
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The source database
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Data modeling for Analysis Services
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Physical database design for Analysis Services
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Summary
2
Building Basic Dimensions and Cubes
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Building Basic Dimensions and Cubes
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Multidimensional and Tabular models
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Choosing an edition of Analysis Services
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Setting up a new Analysis Services project
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Creating data sources
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Creating Data Source Views
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Designing simple dimensions
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Building a simple cube
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Summary
3
Designing More Complex Dimensions
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Designing More Complex Dimensions
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Grouping and banding
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Modeling Slowly Changing Dimensions
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Modeling junk dimensions
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Modeling ragged hierarchies
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Summary
4
Measures and Measure Groups
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Measures and Measure Groups
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Measures and aggregation
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Measure groups
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Dimension/measure group relationships
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Summary
5
Handling Transactional-Level Data
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Handling Transactional-Level Data
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Details about transactional data
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Drillthrough
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Many-to-many dimension relationships
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Summary
6
Adding Calculations to the Cube
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Adding Calculations to the Cube
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Different kinds of calculated members
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Common calculations
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Calculation dimensions
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Named sets
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Summary
7
Adding Currency Conversion
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Adding Currency Conversion
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Introduction to currency conversion
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Where to perform currency conversion
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The Add Business Intelligence wizard
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Measure expressions
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Summary
8
Query Performance Tuning
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Query Performance Tuning
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Understanding how Analysis Services processes queries
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Performance tuning methodology
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Designing for performance
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Performance-specific design features
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MDX calculation performance
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Caching
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Scale-up and Scale-out
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Summary
9
Securing the Cube
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Securing the Cube
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Sample security requirements
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Analysis Services security features
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Administrative security
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Data security
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Dynamic cell security
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Accessing Analysis Services from outside a domain
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Managing security
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Security and query performance
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Summary
10
Going in Production
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Going in Production
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Making changes to a cube in production
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Managing partitions
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Managing processing
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SSAS Data Directory maintenance
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Performing database backup
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Copying databases between servers
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Summary
11
Monitoring Cube Performance and Usage
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Monitoring Cube Performance and Usage
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Analysis Services and the operating system
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Monitoring processing performance
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Monitoring query performance
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Monitoring usage
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Building a complete monitoring solution
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Summary
1
DAX Query Support
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DAX Query Support
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Implementation details
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Connecting Power View to a Multidimensional model
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Running DAX queries against a Multidimensional model
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Index
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Index

Caching

We've already seen how Analysis Services can cache the values returned in the cells of a query, and how this can have a significant impact on the performance of a query. Both the Formula Engine and the Storage Engine can cache data, but may not be able to do so in all circumstances; similarly, although Analysis Services can share the contents of the cache between users there are several situations where it is unable to do so. Given that in most cubes there will be a lot of overlap in the data that users are querying, caching is a very important factor in the overall performance of the cube and as a result ensuring that as much caching as possible is taking place is a good idea.

Formula cache scopes

There are three different cache 'contexts' within the Formula Engine, which relate to how long data can be stored within the cache and how that data can be shared between users:

  • Query Context: This means that the results of calculations can only be cached for the lifetime of a single query...

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