SQL Server Analysis Services: Models

SQL Server Analysis Services (SSAS) provides powerful analytical capabilities, and its core foundation is built upon different types of data models. Understanding these models is crucial for designing, developing, and deploying effective business intelligence solutions.

SSAS primarily supports two distinct modeling paradigms:

1. Multidimensional Models

Multidimensional models are the traditional and most mature modeling approach in Analysis Services. They are based on the concept of data cubes, which are pre-aggregated data structures optimized for fast querying and analysis of large datasets. These models are ideal for complex business scenarios requiring deep slice-and-dice capabilities and sophisticated calculations.

  • Key Concepts: Cubes, Dimensions, Hierarchies, Measures, Perspectives, Calculated Members, MDX (Multidimensional Expressions).
  • Advantages: High performance for complex aggregations, mature tooling, extensive support for advanced calculations.
  • Use Cases: Financial reporting, sales analysis, operational dashboards with complex business logic.

2. Tabular Models

Tabular models represent a newer, in-memory, relational modeling approach for Analysis Services. They leverage a columnar database engine and a highly efficient in-memory analytics engine (VertiPaq) to provide rapid query performance on large datasets. Tabular models are generally easier to develop and manage, especially for users familiar with relational database concepts.

  • Key Concepts: Tables, Columns, Relationships, Measures, Calculated Columns, DAX (Data Analysis Expressions), Row-Level Security.
  • Advantages: Simpler development experience, excellent performance for interactive analysis, integration with Power BI.
  • Use Cases: Self-service BI, interactive dashboards, reporting requiring flexible data exploration.
Key Difference: Multidimensional models are based on star or snowflake schemas with pre-aggregated data structures (cubes), while Tabular models use a relational model with in-memory processing and optimized DAX queries.

Choosing the Right Model

The choice between a multidimensional and a tabular model depends on several factors:

  • Complexity of Calculations: For highly complex, recursive, or intricate business logic, multidimensional models with MDX might be more suitable.
  • Development Skillset: Developers familiar with relational databases and DAX might find tabular models easier to learn and implement.
  • Performance Requirements: Both models offer high performance, but tabular models often excel in interactive, ad-hoc analysis due to their in-memory nature.
  • Integration Needs: Tabular models have tighter integration with Power BI and other modern BI tools.
  • Existing Infrastructure: If you have existing multidimensional solutions, you might want to maintain consistency.

Hybrid Scenarios

It's also possible to deploy both multidimensional and tabular models within the same Analysis Services instance, allowing you to leverage the strengths of each for different business needs.

Explore the links in the sidebar to dive deeper into each model type, understand their architecture, and learn how to develop and manage them effectively.

Further Reading