Querying Azure Analysis Services

This document provides comprehensive guidance on how to query data within your Azure Analysis Services models. We will cover the primary query languages, tools, and best practices to effectively retrieve insights from your tabular and multidimensional models.

Supported Query Languages

Azure Analysis Services supports two primary query languages:

• DAX (Data Analysis Expressions): Primarily used for querying tabular models. DAX is a formula language that allows you to define calculations and queries over tabular data. It is powerful, expressive, and shares many functions with Excel formulas.
• MDX (Multidimensional Expressions): Primarily used for querying multidimensional models. MDX is a query language for OLAP (Online Analytical Processing) cubes. It is designed for navigating hierarchical data structures and performing complex aggregations.

Connecting to Azure Analysis Services

You can connect to your Azure Analysis Services models using various client tools. Some of the most common ones include:

• SQL Server Management Studio (SSMS): A powerful graphical tool for managing and querying Analysis Services.
• Visual Studio (with Analysis Services projects): For developing and deploying models, and also for querying during development.
• Power BI Desktop: A business analytics service that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.
• Excel: Users can connect to Analysis Services models from Excel to create PivotTables and reports.
• Custom Applications: Using ADOMD.NET, AMO (Analysis Management Objects), or OLE DB providers.

Connecting with SSMS

To connect using SSMS:

1. Open SSMS and select "Analysis Services" as the server type.
2. Enter your Azure Analysis Services server name (e.g., your-server-name.windows.net).
3. Choose an authentication method (Windows Authentication or Azure Active Directory).
4. Click "Connect".

Writing DAX Queries

DAX queries are typically written using the EVALUATE statement, followed by a DAX expression that returns a table.

EVALUATE
{
    CALCULATETABLE(
        Sales,
        'Date'[Year] = 2023
    )
}
ORDER BY [Sales Amount] DESC

Common DAX Query Scenarios

• Retrieving all rows from a table: EVALUATE MyTable
• Filtering data: EVALUATE FILTER(MyTable, MyTable[Column] > 100)
• Performing calculations and aggregations: EVALUATE SUMMARIZECOLUMNS('Product'[Category], 'Sales'[SalesAmount])

Writing MDX Queries

MDX queries are structured to select data from cube dimensions, hierarchies, and measures.

SELECT
  {[Measures].[Internet Sales Amount]} ON COLUMNS,
  {([Date].[Calendar Year].&[2023] * [Product].[Category].Children)} ON ROWS
FROM [Adventure Works]`

Common MDX Query Scenarios

• Selecting measures: SELECT {[Measures].[Sales]} ON COLUMNS FROM MyCube
• Navigating hierarchies: SELECT {[Geography].[Country].Members} ON ROWS FROM MyCube
• Slicing and dicing data: SELECT {[Measures].[Sales]} ON COLUMNS, {[Product].[Category].[Bikes].Children} ON ROWS FROM MyCube WHERE ([Date].[Calendar Year].&[2023])

Querying Best Practices

• Understand your model: Familiarize yourself with the tables, columns, relationships, and measures in your model.
• Use appropriate query language: Choose DAX for tabular models and MDX for multidimensional models.
• Optimize queries: Write efficient queries that retrieve only the necessary data. Avoid full table scans where possible.
• Leverage calculated measures: Define complex aggregations as measures within your model rather than recalculating them in every query.
• Filter early and effectively: Apply filters as early as possible in your query to reduce the dataset size processed.

Tools and Resources

Here are some helpful resources for learning more about querying Azure Analysis Services:

• DAX Guide
• MDX Syntax Reference
• Azure Analysis Services Documentation