Modeling in Azure Analysis Services

This section covers the principles and practices for designing and implementing robust data models within Azure Analysis Services (AAS). Effective modeling is crucial for delivering high-performance analytical solutions that meet business requirements.

Tabular Models

Tabular models represent data in a relational structure, similar to a relational database, but optimized for analytical querying. They use an in-memory column-based engine (VertiPaq) for fast query performance.

Key concepts include:

• Tables: Represent collections of related data, much like tables in a relational database.
• Relationships: Define how tables are connected, enabling the engine to traverse data across different sources.
• Measures: Calculations performed on data, typically using DAX (Data Analysis Expressions).
• Calculated Columns: Columns added to tables that contain expressions evaluated row by row.
• Hierarchies: Allow users to navigate data at different levels of detail (e.g., Year > Quarter > Month).

Creating Tabular Models

You can create and manage tabular models using tools such as:

• SQL Server Data Tools (SSDT) for Visual Studio.
• Visual Studio Code with the Tabular Editor extension.

-- Example DAX for a simple SUM measure
TotalSales = SUM(Sales[SalesAmount])

Dimensional Models (Multidimensional)

Dimensional models, also known as OLAP cubes, organize data into facts and dimensions. They are built using a multidimensional OLAP engine.

Key components:

• Fact Tables: Contain transactional or event data (e.g., sales transactions, order details).
• Dimension Tables: Provide context to fact data (e.g., customers, products, dates).
• Measures: Aggregations of numerical data in fact tables.
• Cubes: The primary structure that brings together facts and dimensions for analysis.

Creating Dimensional Models

Dimensional models are typically developed using SQL Server Data Tools (SSDT) for Visual Studio.

Design Considerations

A well-designed model is fundamental to performance and usability. Consider the following:

• Star Schema vs. Snowflake Schema: Understand the trade-offs between these schema designs for tabular models. A star schema is generally preferred for performance.
• Data Granularity: Determine the lowest level of detail required for your analysis.
• Naming Conventions: Use clear and consistent names for tables, columns, measures, and other model objects.
• Data Types: Select appropriate data types to optimize storage and query performance.
• Partitioning: For very large datasets, consider partitioning tables to improve data management and query performance.
• Row-Level Security: Implement security policies to restrict data access based on user roles.

Performance Tuning

Optimizing your AAS model for performance is key. Techniques include:

• DAX Optimization: Write efficient DAX expressions for measures and calculated columns.
• Data Model Design: Denormalize data where appropriate (star schema), avoid unnecessary calculated columns, and use appropriate data types.
• Aggregations: Create aggregations to pre-calculate summaries for frequently queried data.
• Partitioning: Effectively manage large tables through partitioning.
• Indexing: While the VertiPaq engine handles much of this automatically, understanding data cardinality is important.
• Query Tuning: Analyze and optimize the queries sent to the AAS model.

For detailed guidance on specific modeling techniques and DAX functions, refer to the official Microsoft DAX documentation and Tabular Editor documentation.