Multidimensional Models Design Guidance

This section provides guidance for designing and implementing robust and scalable multidimensional models in SQL Server Analysis Services (SSAS).

Introduction to Multidimensional Modeling

Multidimensional modeling is a powerful technique for organizing and analyzing business data. It structures data into cubes, which consist of dimensions and measures, allowing for efficient querying and business intelligence reporting. Understanding the core concepts is crucial for effective design.

Key Design Principles

  • Dimensionality: Understand the business entities that form the basis of your dimensions (e.g., Time, Geography, Product, Customer).
  • Granularity: Define the lowest level of detail for your fact data. This impacts performance and the types of analysis possible.
  • Measures: Identify the business metrics you want to analyze (e.g., Sales Amount, Quantity Sold, Profit).
  • Hierarchies: Design logical drill-down paths within dimensions to enable exploration from high-level summaries to detailed data.
  • Relationships: Establish clear relationships between dimensions and fact tables.

Designing Dimensions

Dimensions provide the context for your data. Well-designed dimensions are essential for intuitive user experience and powerful analysis.

Types of Dimensions

  • Symmetric Dimensions: Dimensions that share a common attribute.
  • Asymmetric Dimensions: Dimensions with different attributes or hierarchies.
  • Stably Sized Dimensions: Dimensions with a predictable number of members.
  • Junk Dimensions: Used to store degenerate dimensions and low-cardinality attributes.

Dimension Attributes and Hierarchies

Organize attributes logically into hierarchies. Consider different hierarchy types, such as:

  • Parent-Child Hierarchies
  • Ragged Hierarchies
  • Unbalanced Hierarchies

Designing Fact Tables

Fact tables store the quantitative measures and foreign keys to dimensions. The design of your fact table directly impacts the granularity and scope of your analysis.

Fact Table Types

  • Transaction Fact Tables
  • Periodic Snapshot Fact Tables
  • Accumulating Snapshot Fact Tables

Grain Definition

Accurately defining the grain of your fact table is paramount. A common mistake is having a grain that is too high or too low, limiting analytical capabilities or causing performance issues.

Performance Tuning and Best Practices

Achieving optimal performance is critical for a responsive BI solution.

  • Partitioning: Strategically partition large fact tables to improve query performance and manageability.
  • Aggregations: Pre-calculate aggregated data to speed up common queries.
  • Indexing: Understand how Analysis Services uses internal indexing mechanisms.
  • Data Types: Use appropriate data types for measures and attributes.
  • MDX Optimization: Write efficient Multidimensional Expressions (MDX) queries.

Example MDX Query Snippet


SELECT
  {[Measures].[Internet Sales Amount]} ON COLUMNS,
  {[Date].[Calendar Year].MEMBERS} ON ROWS
FROM [Adventure Works DW]
WHERE ([Product].[Category].&[1])

Advanced Design Considerations

  • Kpis (Key Performance Indicators): Define and track key business metrics.
  • Perspectives: Create focused views of your cube for different user groups.
  • Translations: Support multilingual metadata.
  • Security: Implement row-level and object-level security.

See Also