SQL Server Analysis Services: Tabular Modeling

Introduction to Tabular Modeling

Tabular modeling is a powerful approach within SQL Server Analysis Services (SSAS) that allows you to create business intelligence semantic models using an in-memory columnar database. It's designed for rapid development and high performance, making it ideal for interactive analysis and self-service BI scenarios. Unlike the multidimensional model, tabular models are built on a relational in-memory database engine that leverages VertiPaq technology for efficient data compression and query performance.

This model offers a familiar structure for users accustomed to relational databases and business logic development, enabling them to build sophisticated analytical solutions more intuitively.

Key Features and Benefits

  • In-Memory Performance: VertiPaq engine provides lightning-fast query responses.
  • Simplified Development: Uses a familiar relational metadata structure.
  • DAX Language: Powerful Data Analysis Expressions (DAX) for complex calculations.
  • Integration: Seamless integration with Power BI, Excel, and other BI tools.
  • Scalability: Designed to handle large datasets and concurrent users.
  • Tooling: Developed using tools like Visual Studio with SSDT or by directly authoring in Power BI Desktop.

Building a Tabular Model

Developing a tabular model typically involves the following steps:

  1. Data Source Connection: Connect to relational databases (SQL Server, Oracle, etc.), Azure SQL Database, or other supported data sources.
  2. Table Import: Import data from your sources into the tabular model workspace.
  3. Table Relationships: Define relationships between tables, similar to relational database schemas.
  4. Calculations: Create calculated columns, measures, and KPIs using DAX.
  5. Roles and Security: Implement row-level security using roles.
  6. Deployment: Deploy the model to an SSAS instance or an Azure Analysis Services server.

Tools such as Visual Studio with SQL Server Data Tools (SSDT) or Power BI Desktop (which can deploy to Azure Analysis Services) are commonly used for model authoring.

Data Analysis Expressions (DAX)

DAX is the formula language used in tabular models for creating calculations. It's a powerful functional language that enables sophisticated data analysis. Common DAX functions include:

  • Aggregation functions (e.g., SUM, AVERAGE, COUNT)
  • Iterators (e.g., SUMX, AVERAGEX)
  • Time intelligence functions (e.g., CALCULATE, DATEADD, SAMEPERIODLASTYEAR)
  • Logical functions (e.g., IF, SWITCH)
  • Filter context manipulation functions (e.g., FILTER, ALL, ALLEXCEPT)

For example, a simple measure to calculate total sales might look like this: 

Total Sales := SUM( 'Sales'[SalesAmount] )

And a more complex measure using CALCULATE and time intelligence: 

Year-over-Year Sales Growth :=
DIVIDE(
    [Total Sales] - CALCULATE( [Total Sales], DATEADD( 'Date'[Date], -1, YEAR ) ),
    CALCULATE( [Total Sales], DATEADD( 'Date'[Date], -1, YEAR ) )
)

Deployment and Usage

Once developed, a tabular model can be deployed to:

  • SQL Server Analysis Services (SSAS): On-premises or on a virtual machine.
  • Azure Analysis Services: A fully managed cloud BI service.
  • Power BI Premium/Embedded: For hosting large datasets and enterprise-grade analytics.

Deployed models can then be connected to by various client tools, including Power BI Desktop, Excel PivotTables, and custom reporting applications, to perform interactive analysis.

Considerations: Tabular modeling is often preferred for its ease of use, rapid development cycle, and excellent performance with in-memory processing. It's a strong choice for scenarios requiring agility and direct integration with modern BI tools like Power BI.