Multidimensional Models in Azure Analysis Services

Updated: October 26, 2023

Azure Analysis Services provides a powerful platform for building enterprise-grade semantic models for business intelligence applications. This document focuses on the multidimensional model, a traditional yet robust approach to data modeling.

What are Multidimensional Models?

Multidimensional models represent data in a cube-like structure, allowing for efficient querying and analysis. Key components include:

  • Dimensions: Categories used to filter and group data (e.g., Time, Geography, Product).
  • Measures: Numeric values that can be aggregated (e.g., Sales Amount, Quantity).
  • Cubes: The central repository for dimensions and measures, enabling multidimensional analysis.
  • Hierarchies: Structured levels within a dimension that allow for drill-down and roll-up operations (e.g., Year > Quarter > Month).

Creating a Multidimensional Model

You can create multidimensional models using SQL Server Data Tools (SSDT) or Visual Studio with the Analysis Services projects extension.

Using SQL Server Data Tools (SSDT)

SSDT provides a rich, visual environment for designing and deploying Analysis Services solutions. The process typically involves:

  1. Creating a new Analysis Services Multidimensional Project.
  2. Connecting to your data sources.
  3. Designing the database schema, including tables, relationships, dimensions, and measures.
  4. Deploying the model to your Azure Analysis Services instance.
Ensure you have the latest version of SQL Server Data Tools installed for optimal compatibility with Azure Analysis Services.

Key Concepts and Features

Dimensions

Dimensions are crucial for slicing and dicing data. You can create various types of dimensions:

  • Date Dimensions: Automatically generated or manually defined for temporal analysis.
  • Geography Dimensions: Support location-based analysis.
  • Attribute Relationships: Define dependencies between attributes within a dimension for optimized query performance.

Measures and Aggregations

Measures are the core of your analytical data. You can define different aggregation functions (SUM, COUNT, AVG, etc.) and create calculated measures for more complex scenarios.

For frequently used aggregations, consider defining pre-aggregated measures in your source data or using aggregation designs within Analysis Services to improve query performance.

Partitions

Partitions allow you to divide a cube into smaller, manageable pieces, improving query performance and manageability, especially for large datasets. You can partition based on time, region, or other criteria.

Performance Tuning

Optimizing the performance of your multidimensional models is key to delivering a responsive BI experience. Consider the following:

  • Aggregation Designs: Create and manage aggregation designs to pre-calculate common aggregations.
  • Indexing: Properly index your source data tables.
  • Dimension Table Optimization: Ensure dimension tables are well-structured and avoid unnecessary joins.
  • Query Optimization: Analyze and tune your DAX or MDX queries.

Best Practices

Adhering to best practices will ensure your multidimensional models are scalable, maintainable, and performant:

  • Design with the end-user in mind, focusing on a user-friendly business logic layer.
  • Keep dimensions and measures organized and logically grouped.
  • Regularly review and update aggregation strategies.
  • Implement a robust testing strategy for deployments.

Learn More

For more in-depth information and tutorials, explore the following resources: