Tabular vs. Multidimensional Models in SQL Server Analysis Services

SQL Server Analysis Services (SSAS) provides two primary modeling approaches for business intelligence solutions: Tabular models and Multidimensional models. Each offers a distinct architecture and set of capabilities, catering to different user needs and scenarios.

Key Differences

The fundamental distinction lies in their underlying architecture and how data is organized and queried.

Multidimensional Models

Multidimensional models are built around the concept of cubes. These cubes are pre-aggregated data structures that allow for fast querying and slicing and dicing of data across various dimensions. They are well-suited for complex analytical scenarios with deeply nested hierarchies and extensive aggregations.

• Architecture: Cube-based, with facts and dimensions.
• Data Storage: Optimized for aggregations, allowing for very fast query performance on pre-calculated data.
• Tooling: Traditionally managed using SQL Server Data Tools (SSDT) with specific multidimensional project types.
• Languages: Primarily use Multidimensional Expressions (MDX) for querying and Data Definition Language (DDL) for model definition.
• Performance: Excellent for complex OLAP queries and scenarios requiring extensive pre-aggregation.
• Flexibility: Can be more rigid in terms of schema changes once deployed due to extensive pre-computation.

Tabular Models

Tabular models, on the other hand, use an in-memory columnar database engine (VertiPaq) that stores data in a highly compressed format. This approach is often seen as more intuitive, especially for users familiar with relational databases. Tabular models are highly performant due to their in-memory nature and efficient compression.

• Architecture: Relational-like, using tables, columns, and relationships.
• Data Storage: In-memory columnar database, optimized for high performance with compression and modern hardware.
• Tooling: Managed using SSDT with tabular project types, and increasingly with tools like Power BI Desktop.
• Languages: Primarily use Data Analysis Expressions (DAX) for querying and calculations, and a JSON-based metadata format for model definition.
• Performance: Excellent for interactive analysis, ad-hoc queries, and scenarios that benefit from in-memory processing.
• Flexibility: Generally more agile and easier to modify, especially during development.

Comparison Table

Feature	Multidimensional Models	Tabular Models
Core Concept	Cubes (pre-aggregated data)	In-memory relational-like tables
Query Language	MDX	DAX
Data Engine	OLAP engine	VertiPaq (in-memory columnar)
Ease of Use	Steeper learning curve, powerful for experts	More intuitive for relational developers and analysts
Development Tools	SSDT (Multidimensional projects)	SSDT (Tabular projects), Power BI Desktop
Aggregation Strategy	Pre-aggregation (cubes)	On-the-fly calculation and compression
Scalability	Scales well, especially with partitioning	Scales well, benefits from RAM
Typical Use Cases	Complex corporate reporting, pre-defined KPIs, deep hierarchies	Interactive dashboards, self-service BI, ad-hoc analysis, modern reporting

When to Choose Which

The choice between tabular and multidimensional models often depends on the specific requirements of your project, the skills of your development team, and the needs of your end-users.

Choose Multidimensional if:

• You have deeply nested hierarchies that require extensive pre-aggregation for performance.
• Your organization has a long history of using and supporting multidimensional models and MDX.
• You need to support complex business logic and calculations that are best expressed in MDX.
• Performance for highly predictable, complex analytical queries is paramount.

Choose Tabular if:

• You want a more intuitive development experience, similar to relational databases.
• Your users are accustomed to tools like Excel or Power BI, which integrate seamlessly with tabular models.
• Agile development and faster iteration cycles are important.
• You are building modern interactive dashboards and self-service BI solutions.
• Memory is readily available and can be leveraged for excellent performance.