Azure Analysis Services Documentation

Designing Models in Azure Analysis Services

This document provides comprehensive guidance on designing robust and efficient data models for Azure Analysis Services (AAS). A well-designed model is crucial for delivering high-performance business intelligence solutions.

Introduction to AAS Models

Azure Analysis Services models are tabular data models that store and manage business data. They are built on the Microsoft Analysis Services engine, optimized for analytical queries. The primary goal is to transform raw data into a format that is easily understood and queried by business users and applications.

Understanding Tabular Model Compatibility Levels

Azure Analysis Services supports different tabular model compatibility levels. Newer levels offer enhanced features and performance. Choosing the right compatibility level depends on your needs and the features you want to leverage.

• 1100/1103: Standard tabular model compatibility levels.
• 1200 and higher: Offer advanced features like DirectQuery for SQL Server, improved performance, and more DAX functions.

It is generally recommended to use the latest compatibility level supported by your version of SQL Server Data Tools (SSDT) or Visual Studio to take advantage of the newest features and optimizations.

Best Practices for Model Design

Adhering to best practices ensures your model is performant, scalable, and maintainable:

• Keep Models Lean: Only include necessary tables and columns. Remove redundant data.
• Optimize Table and Column Names: Use clear, business-friendly names. Avoid special characters.
• Use Appropriate Data Types: Select the most efficient data type for each column.
• Denormalize Strategically: While relational databases are normalized, tabular models benefit from some denormalization for query performance.
• Implement Row-Level Security (RLS): Control data access based on user roles.
• Use Hierarchies: Create logical drill-down paths for easier navigation.
• Develop Robust DAX Measures: Write efficient DAX for calculations and aggregations.
• Test Performance Regularly: Use performance monitoring tools to identify bottlenecks.

Tools for Model Design

Several tools can be used to design and deploy Azure Analysis Services models:

• SQL Server Data Tools (SSDT) for Visual Studio: The primary development tool for creating, managing, and deploying tabular models.
• Visual Studio with Analysis Services Projects: Offers a rich development environment for tabular models.
• Tabular Editor: A popular third-party tool for advanced metadata editing, scripting, and optimization of tabular models.
• Azure portal: For managing the Azure Analysis Services instance itself, monitoring, and basic configuration.

Designing Tables and Columns

The foundation of your model is its tables and columns. Consider the following:

• Fact Tables: Contain transactional data, typically measures (numeric values) and foreign keys to dimension tables.
• Dimension Tables: Contain descriptive attributes used for filtering and grouping data (e.g., Date, Product, Customer).
• Date Tables: Essential for time-intelligence calculations. Ensure it has a contiguous date range and appropriate columns for year, month, day, quarter, etc.
• Column Properties:
  • Data Type: Choose wisely (e.g., Integer for IDs, Decimal for currency, DateTime for dates).
  • Format: Apply consistent formatting for numbers, dates, and currencies.
  • IsHidden: Hide columns that are not meant for direct user interaction but are used for relationships or calculations.

Defining Relationships

Relationships link tables together, enabling data traversal and filtering. In tabular models, these are typically one-to-many or one-to-one.

• Cardinality: Ensure correct cardinality (e.g., one-to-many from dimension to fact).
• Cross-Filter Direction: Understand how filters propagate. Typically, you want filters from dimension tables to flow to fact tables.
• Enable/Disable Relationships: Use this sparingly for advanced scenarios.

Creating Calculations with DAX

Data Analysis Expressions (DAX) is the formula language used in tabular models for creating calculated columns, measures, and row-level security roles.

• Measures: Calculations that aggregate data dynamically based on the current filter context. Examples include SUM, AVERAGE, COUNT, CALCULATE.
• Calculated Columns: Calculations performed row-by-row and stored in the model. Use them judiciously as they increase model size and memory footprint.
• Time Intelligence Functions: DAX provides powerful functions for year-to-date, month-to-date, prior-period comparisons, etc. (e.g., DATESYTD, SAMEPERIODLASTYEAR).

Example DAX Measure for Total Sales:

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

Designing Hierarchies

Hierarchies provide a logical drill-down path in reporting tools, mimicking business structures like Date (Year > Quarter > Month > Day) or Geography (Country > Region > City).

• Create hierarchies in SSDT/Visual Studio by dragging and dropping columns in the desired order.
• Ensure the hierarchy levels are granular and logically ordered.

Implementing Security

Azure Analysis Services supports two primary security mechanisms:

• Database Roles: Define roles with specific read or read/write permissions on the model database.
• Row-Level Security (RLS): Use DAX expressions within roles to filter data visible to specific users or groups. This is crucial for ensuring users only see data relevant to them.

Example RLS DAX for a sales role to see only sales for a specific region:

[Region] = "North"
            

By following these guidelines, you can design effective and performant data models in Azure Analysis Services that empower your organization's analytical needs.