SQL Server Analysis Services 2012 Cube Development Cookbook

SQL Server Analysis Services 2012 Cube Development Cookbook
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Table of Contents
Sample Chapters
  • Develop Business Intelligence solutions using a multi-dimensional model as well as a tabular model
  • Explore cube maintenance with partitions and design effective aggregations, as well as analyzing options for scaling analytics solutions
  • Includes recipes for administering, securing, monitoring, and troubleshooting Analysis Services solutions

Book Details

Language : English
Paperback : 340 pages [ 235mm x 191mm ]
Release Date : December 2013
ISBN : 1849689806
ISBN 13 : 9781849689809
Author(s) : Baya Dewald, Paul Turley, Steve Hughes
Topics and Technologies : All Books, Big Data and Business Intelligence, Cookbooks, Enterprise

Table of Contents

Chapter 1: Introduction to Multidimensional Data Model Design
Chapter 2: Defining Analysis Services Dimensions
Chapter 3: Creating Analysis Services Cubes
Chapter 4: Extending and Customizing Cubes
Chapter 5: Optimizing Dimension and Cube Processing
Chapter 6: MDX
Chapter 7: Analysis Services Security
Chapter 8: Administering and Monitoring Analysis Services
Chapter 9: Using Tabular Models
Chapter 10: DAX Calculations and Queries
Chapter 11: Performance Tuning and Troubleshooting Tabular Models
Appendix: Miscellaneous Analysis Services Topics
  • Chapter 1: Introduction to Multidimensional Data Model Design
    • Introduction
    • The business value of Business Intelligence
    • Challenges and barriers of effective BI
    • Overcoming BI challenges and barriers
    • Choosing multidimensional or Tabular Models
    • Star- or Snowflake-relational schema
    • A sample scenario for choosing the Snowflake schema
  • Chapter 2: Defining Analysis Services Dimensions
    • Introduction
    • Defining data sources
    • Defining data source views
    • Defining entity relationships in DSV
    • Extending data source views
    • Creating named calculations and queries
    • Creating simple dimensions
    • Building dimension hierarchies
    • Setting essential attribute properties
    • Browsing dimension data
    • Sorting the attributes
    • Customizing advanced attribute properties
    • Creating parent-child dimensions
    • Creating the date and time dimensions
  • Chapter 3: Creating Analysis Services Cubes
    • Introduction
    • Defining measure groups and measures
    • Setting measure properties
    • Browsing the cube data
    • Dimension usage with measure group
    • Examining cube file structures
    • Partitioning strategies
    • Defining partition slice
    • Merging partitions
    • Defining aggregation designs
    • Distinct count measure groups
    • Enabling write-back feature
    • Deployment options
  • Chapter 4: Extending and Customizing Cubes
    • Introduction
    • Defining calculated measures
    • Defining named sets
    • Defining drillthrough actions
    • Defining URL actions
    • Defining reporting actions
    • Defining key performance indicators
    • Defining perspectives
    • Defining translations
    • Defining measure expressions
  • Chapter 5: Optimizing Dimension and Cube Processing
    • Introduction
    • Understanding dimension processing options
    • Learning about basic dimension processing
    • Learning advanced dimension processing options
    • Using out-of-line bindings for dimension processing
    • Dealing with partition processing options
    • Using SQL Server Integration Services to process Analysis Services objects
    • Monitoring and tuning processing performance
  • Chapter 6: MDX
    • Introduction
    • Returning data on the query axes
    • Limiting the query output
    • Sorting the query output
    • Defining query level calculations and named sets
    • Navigating dimension hierarchies
    • Working with the Time dimensions
    • MDX script's functionality
    • Monitoring and tuning MDX queries
  • Chapter 7: Analysis Services Security
    • Introduction
    • Managing instance-level administrative security
    • Managing database-level security
    • Managing cube-level security
    • Managing dimension hierarchy-level security
    • Implementing dynamic dimension security
    • Implementing cell-level security
  • Chapter 8: Administering and Monitoring Analysis Services
    • Introduction
    • SSAS instance configuration options
    • Creating and dropping databases
    • Monitoring SSAS instance using Activity Viewer
    • Monitoring SSAS instance using DMVs
    • Cancelling a session
    • Checking whether cubes are accessible
    • Checking SSAS object sizes programmatically
    • Backup and restore
    • Synchronizing databases
    • Detaching and attaching databases
  • Chapter 9: Using Tabular Models
    • Introduction
    • Creating a Tabular Model
    • Working with data sources and loading data
    • Modeling the data
    • Creating a hierarchy
    • Creating a calculated measure
    • Creating a calculated column
    • Creating a KPI
    • Analyzing your model in Excel
    • Deploying Tabular Models
    • Scripting Tabular Models using XMLA
    • Processing Tabular Models
    • Partitioning Tabular Models
    • Implementing perspectives
    • Implementing security in Tabular Models
    • Automating Tabular Model processing
  • Chapter 10: DAX Calculations and Queries
    • Introduction
    • Combining tables using calculated columns
    • Adding a calculated column
    • Creating measures
    • Testing a Tabular Model in Excel
    • Using the CALCULATE function
    • Querying a Tabular Model
  • Appendix: Miscellaneous Analysis Services Topics
    • Working with non-SQL Server data sources
    • Common yet confusing SSAS errors
    • Dimension properties
    • Performance considerations for many-to-many dimension relationships
    • DirectQuery with Tabular Models

Baya Dewald

Baya Dewald has been working with Microsoft SQL Server for over 15 years and with Analysis Services since its debut in 1999. He has architected, developed, and administered databases with millions of users and cubes measured in terabytes. Throughout his career, he has held various certifications, including Microsoft Certified Solution Developer, Microsoft Certified Systems Engineer, and Microsoft Certified Database Administrator. He has managed DBA teams and published over 100 technical articles in SQL Server Magazine, SQL Professional, Tech Target, and InformIT. He has also contributed to several technical books as a co-author and technical reviewer. He can be reached at baya.dewald@gmail.com.

Paul Turley

Paul Turley is a mentor with SolidQ and is a Microsoft SQL Server MVP. He consults, writes, speaks, teaches, and blogs about business intelligence and reporting solutions. He works with companies around the world to visualize and deliver critical information to make informed business decisions. He is the director of the Oregon SQL PASS chapter and user group, and the lead author of Professional SQL Server 2012 Reporting Services and 11 other books by Wrox and Microsoft Press. Paul blogs at SqlServerBiBlog.com.

Steve Hughes

Steve Hughes is a Practice Lead at Magenic. In his current role, he develops strategy and helps guide data, business intelligence, collaboration, and data integration development using Microsoft technologies, including SQL Server, SharePoint, and BizTalk. He continues to deliver data and business intelligence solutions using these platforms. He has been working with technology for over 15 years with much of that time spent on creating business intelligence solutions. He is passionate about using data effectively and helping customers understand that data is valuable and profitable. He also has experience working with a large software company as a service vendor and there received awards for customer satisfaction and engineering excellence.

In 2010, he received the Consultant of the Year honor at Magenic. He has delivered multiple presentations on SQL server, data architecture, and business intelligence over the years. Steve can often be found at Professional Association for SQL Server (PASS) events where he serves as a regional mentor and is active with the Minnesota SQL Server User Group (MNPASS). He shares his insights from the field on his blog at http://dataonwheels.wordpress.com.

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Sample chapters

You can view our sample chapters and prefaces of this title on PacktLib or download sample chapters in PDF format.

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What you will learn from this book

  • Design commonly encountered dimensions
  • Extend your cubes to include advanced features such as actions, translations, and key performance indicators
  • Maintain cubes by adding/removing data using partitions
  • Administer Analysis Services instance
  • Scale out your solution by synchronizing cube data
  • Build tabular model solutions

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.


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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