This section provides comprehensive documentation on designing and implementing multidimensional data models within SQL Server Analysis Services (SSAS). Multidimensional models are built around cubes, which offer a highly efficient way to analyze large amounts of data from various perspectives.

Core Concepts

Understand the fundamental building blocks of SSAS multidimensional models:

Dimensions Measures Cubes Hierarchies Key Performance Indicators (KPIs)

Dimensions

Dimensions represent the descriptive attributes of your data. They provide the context for your measures and allow users to slice and dice data. Common examples include Time, Geography, Products, and Customers.

  • Creating Dimensions: Learn best practices for defining dimension tables and attributes.
  • Attribute Relationships: Understand how to define relationships between attributes within a dimension for optimized query performance.
  • User-Defined Hierarchies: Structure attributes into logical hierarchies for intuitive navigation.

Measures

Measures are the quantitative values you want to analyze, such as Sales Amount, Quantity Sold, or Profit. They are typically aggregated from underlying fact tables.

  • Defining Measures: Explore different aggregation types (Sum, Count, Average, etc.) and calculated measures.
  • Measure Groups: Organize related measures into measure groups for better management.

Cubes

A cube is the central object in a multidimensional model, comprising dimensions and measure groups. It's the primary structure used for online analytical processing (OLAP).

  • Cube Design Best Practices: Strategies for building performant and user-friendly cubes.
  • Dimension Usage: Configuring how dimensions interact with measure groups.

Hierarchies

Hierarchies allow users to explore data at different levels of granularity. They can be natural (e.g., Year > Quarter > Month > Day) or custom-defined.

  • Creating and Managing Hierarchies: Step-by-step guides and examples.

Key Performance Indicators (KPIs)

KPIs provide a standardized way to measure progress towards strategic business objectives. They are often visualized in dashboards.

  • Defining KPIs: Setting up measure expressions and goal values.

Advanced Topics

Dive deeper into more complex aspects of multidimensional modeling:

Performance Tuning Security Models MDX Query Language

Performance Tuning

Optimize your SSAS multidimensional models for speed and efficiency.

-- Example: Creating a calculated measure
CREATE MEMBER CURRENTCUBE.[Measures].[Profit Margin] AS
([Measures].[Profit] / [Measures].[Sales Amount])
,FORMAT_STRING = "Percent"
,VISIBLE = 1 ;
  • Partitioning strategies
  • Caching and aggregation designs
  • Query optimization techniques

Security Models

Implement robust security measures to control data access.

  • Role-based security
  • Row-level security
  • Cell-level security

MDX Query Language

Master the Multidimensional Expressions (MDX) language for querying SSAS cubes.

  • Basic MDX syntax
  • Common MDX functions
  • Building complex queries

Tools and Resources

Discover essential tools and external resources for multidimensional modeling:

  • SQL Server Data Tools (SSDT) for Visual Studio
  • SQL Server Management Studio (SSMS)
  • Community forums and blogs