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Expert Cube Development with SSAS Multidimensional Models. For Analysis Service cube designers this is the hands-on tutorial that will take your expertise to a whole new level. Written by a team of Microsoft SSAS experts, it digs deep to optimize your Business Intelligence capabilities

Expert Cube Development with SSAS Multidimensional Models. For Analysis Service cube designers this is the hands-on tutorial that will take your expertise to a whole new level. Written by a team of Microsoft SSAS experts, it digs deep to optimize your Business Intelligence capabilities

Alberto Ferrari, Marco Russon, Chris Webb

Ebook
  • Expert Cube Development with SSAS Multidimensional Models
    • Table of Contents
    • Expert Cube Development with SSAS Multidimensional Models
    • Credits
    • About the Authors
    • About the Reviewers
    • www.PacktPub.com
      • Support files, eBooks, discount offers and more
        • Why subscribe?
        • Free access for Packt account holders
        • Instant updates on new Packt books
    • Preface
      • What this book covers
      • What you need for this book
      • Who this book is for
      • Conventions
      • Reader feedback
      • Customer support
        • Downloading the example code and database for the book
        • Downloading the color images of this book
        • Errata
        • Piracy
        • Questions
    • 1. Designing the Data Warehouse for Analysis Services
      • The source database
        • The OLTP database
        • The data warehouse
        • The data mart
      • Data modeling for Analysis Services
        • Fact tables and dimension tables
        • Star schemas and snowflake schemas
        • Junk dimensions
        • Degenerate dimensions
        • Slowly Changing Dimensions
        • Bridge tables or factless fact tables
        • Snapshot and transaction fact tables
        • Updating fact and dimension tables
        • Natural and surrogate keys
        • Unknown Members, key errors, and NULLability
      • Physical database design for Analysis Services
        • Multiple data sources
        • Data types and Analysis Services
        • SQL queries generated during cube processing
          • Dimension processing
          • Dimensions with joined tables
          • Reference dimensions
          • Fact dimensions
          • Distinct Count measures
          • Indexes in the data mart
            • Dimension tables
            • Fact tables
        • Usage of schemas
        • Naming conventions
        • Views versus the Data Source View
      • Summary
    • 2. Building Basic Dimensions and Cubes
      • Multidimensional and Tabular models
      • Choosing an edition of Analysis Services
      • Setting up a new Analysis Services project
      • Creating data sources
      • Creating Data Source Views
      • Designing simple dimensions
        • Using the New Dimension wizard
        • Using the Dimension Editor
          • Adding new attributes
          • Configuring a Time dimension
          • Creating user hierarchies
          • Configuring attribute relationships
      • Building a simple cube
        • Using the New Cube wizard
        • Project deployment
        • Database processing
      • Summary
    • 3. Designing More Complex Dimensions
      • Grouping and banding
        • Grouping
        • Banding
      • Modeling Slowly Changing Dimensions
        • Type I SCDs
        • Type II SCDs
          • Modeling attribute relationships on a Type II SCD
          • Handling member status
        • Type III SCDs
      • Modeling junk dimensions
      • Modeling ragged hierarchies
        • Modeling parent/child hierarchies
        • Ragged hierarchies with HideMemberIf
      • Summary
    • 4. Measures and Measure Groups
      • Measures and aggregation
        • Useful properties of measures
          • FormatString
          • DisplayFolders
        • Built-in measure aggregation types
          • Basic aggregation types
          • DistinctCount
          • None
          • Semi-additive aggregation types
          • ByAccount
        • Dimension calculations
          • Unary operators and weights
          • Custom Member Formulas
        • Non-aggregatable values
      • Measure groups
        • Creating multiple measure groups
        • Creating measure groups from dimension tables
          • MDX formulas versus pre-calculating values
        • Handling different dimensionality
        • Handling different granularities
        • Non-aggregatable measures a different approach
        • Using linked dimensions and measure groups
        • Role-playing dimensions
      • Dimension/measure group relationships
        • Fact relationships
        • Referenced relationships
        • Data mining relationships
      • Summary
    • 5. Handling Transactional-Level Data
      • Details about transactional data
      • Drillthrough
        • Actions
        • Drillthrough actions
          • Drillthrough columns order
          • Drillthrough and calculated members
        • Drillthrough modeling
          • Drillthrough using a transaction detail dimension
          • Drillthrough with ROLAP dimensions
          • Drillthrough on alternate fact table
          • Drillthrough recap
      • Many-to-many dimension relationships
        • Implementing a many-to-many dimension relationship
        • Advanced modeling with many-to-many relationships
        • Performance issues
      • Summary
    • 6. Adding Calculations to the Cube
      • Different kinds of calculated members
      • Common calculations
        • Simple calculations
        • Referencing cell values
        • Aggregating members
        • Year-to-date calculations
        • Ratios over a hierarchy
        • Previous period growths
        • Same period previous year
        • Moving averages
        • Ranks
        • Formatting calculated measures
      • Calculation dimensions
        • Implementing a simple calculation dimension
        • The Time Intelligence wizard
          • Attribute overwrite
          • Limitations of calculated members
        • Calculation dimension best practices
      • Named sets
        • Regular named sets
        • Dynamic named sets
      • Summary
    • 7. Adding Currency Conversion
      • Introduction to currency conversion
        • Data collected in a single currency
        • Data collected in a multiple currencies
      • Where to perform currency conversion
      • The Add Business Intelligence wizard
        • Concepts and prerequisites
        • How to use the Add Business Intelligence wizard
          • Data collected in a single currency with reporting in multiple currencies
          • Data collected in multiple currencies with reporting in a single currency
          • Data stored in multiple currencies with reporting in multiple currencies
      • Measure expressions
        • DirectSlice property
        • Writeback
      • Summary
    • 8. Query Performance Tuning
      • Understanding how Analysis Services processes queries
      • Performance tuning methodology
      • Designing for performance
      • Performance-specific design features
        • Partitions
          • Why partition?
          • Building partitions
          • Planning a partitioning strategy
          • Unexpected Partition scans
        • Aggregations
          • Creating an initial aggregation design
          • Usage-Based Optimization
          • Monitoring partition and aggregation usage
          • Building aggregations manually
          • Common aggregation design issues
      • MDX calculation performance
        • Diagnosing Formula Engine performance problems
        • Calculation performance tuning
          • Tuning algorithms used in MDX
            • Using Named Sets to avoid recalculating Set Expressions
          • Using calculated members to cache numeric values
          • Tuning the implementation of MDX
      • Caching
        • Formula cache scopes
        • Other scenarios that restrict caching
        • Cache warming
          • The CREATE CACHE statement
          • Running batches of queries
      • Scale-up and Scale-out
      • Summary
    • 9. Securing the Cube
      • Sample security requirements
      • Analysis Services security features
        • Roles and role membership
        • Securable objects
        • Creating roles
        • Membership of multiple roles
        • Testing roles
      • Administrative security
      • Data security
        • Granting Read Access to Cubes
        • Cell security
        • Dimension security
          • Visual Totals
          • Restricting access to Dimension Members
          • Applying security to Measures
        • Dynamic security
          • Dynamic dimension security
          • Dynamic security with stored procedures
        • Dimension security and parent/child hierarchies
      • Dynamic cell security
      • Accessing Analysis Services from outside a domain
      • Managing security
      • Security and query performance
        • Cell security
        • Dimension security
        • Dynamic security
      • Summary
    • 10. Going in Production
      • Making changes to a cube in production
      • Managing partitions
        • Relational versus Analysis Services partitioning
        • Building a template partition
        • Generating partitions in Integration Services
      • Managing processing
        • Dimension processing
        • Partition processing
        • Lazy Aggregations
        • Processing reference dimensions
        • Handling processing errors
        • Managing processing with Integration Services
        • Push-mode processing
        • Proactive caching
      • SSAS Data Directory maintenance
      • Performing database backup
      • Copying databases between servers
      • Summary
    • 11. Monitoring Cube Performance and Usage
      • Analysis Services and the operating system
        • Resources shared by the operating system
          • CPU
          • Memory
          • I/O Operations
        • Tools to monitor resource consumption
          • Windows Task Manager
          • Performance counters
          • Resource Monitor
        • Analysis Services memory management
          • Memory differences between 32 bit and 64 bit
          • Controlling the Analysis Services Memory Manager
          • Out of memory conditions in Analysis Services
        • Sharing SQL Server and Analysis Services on the same machine
      • Monitoring processing performance
        • Monitoring processing with trace data
          • SQL Server Profiler
          • ASTrace
          • XMLA
          • Flight Recorder
        • Monitoring processing with Performance Monitor counters
        • Monitoring processing with Dynamic Management Views
      • Monitoring query performance
        • Monitoring queries with trace data
        • Monitoring queries with Performance Monitor counters
        • Monitoring queries with Dynamic Management Views
      • Monitoring usage
        • Monitoring usage with trace data
        • Monitoring usage with Performance Monitor counters
        • Monitoring usage with Dynamic Management Views
        • Activity Viewer
      • Building a complete monitoring solution
      • Summary
    • A. DAX Query Support
      • Implementation details
        • Mapping Multidimensional objects to Tabular concepts
        • Unsupported features
        • New functionality in Analysis Services
      • Connecting Power View to a Multidimensional model
      • Running DAX queries against a Multidimensional model
        • Executing DAX queries
        • DAX queries and attributes
    • Index
  • Title: Expert Cube Development with SSAS Multidimensional Models. For Analysis Service cube designers this is the hands-on tutorial that will take your expertise to a whole new level. Written by a team of Microsoft SSAS experts, it digs deep to optimize your Business Intelligence capabilities
  • Author: Alberto Ferrari, Marco Russon, Chris Webb
  • Original title: Expert Cube Development with SSAS Multidimensional Models. For Analysis Service cube designers this is the hands-on tutorial that will take your expertise to a whole new level. Written by a team of Microsoft SSAS experts, it digs deep to optimize your Business Intelligence capabilities.
  • ISBN: 9781849689915, 9781849689915
  • Date of issue: 2014-02-24
  • Format: Ebook
  • Item ID: e_3cjf
  • Publisher: Packt Publishing