Azure Analysis Services

Querying Data in Azure Analysis Services

This document provides a comprehensive guide on how to query data from your Azure Analysis Services models. We will cover the supported query languages and best practices for efficient data retrieval.

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Supported Query Languages

Azure Analysis Services supports two primary query languages for interacting with tabular models:

  • Data Analysis Expressions (DAX): A powerful formula expression language used in Power BI, Analysis Services, and Power Pivot in Excel. DAX is recommended for most modern scenarios due to its rich functionality and integration with Power BI.
  • Multidimensional Expressions (MDX): A query language for OLAP (Online Analytical Processing) cubes. While still supported, DAX is generally preferred for new tabular model development.

Using DAX

DAX allows you to create complex calculations and retrieve data from your model. You can write DAX queries using various tools, including SQL Server Management Studio (SSMS), DAX Studio, and directly within Power BI Desktop.

Example DAX Query

This DAX query returns the total sales amount for each product category:

EVALUATE SUMMARIZECOLUMNS( 'Product'[Category], "Total Sales", SUM('Sales'[SalesAmount]) ) ORDER BY [Total Sales] DESC

Key DAX Concepts:

  • Measures: Pre-defined calculations that aggregate data, like "Total Sales".
  • Calculated Columns: Columns added to tables that perform row-by-row calculations.
  • Relationships: How tables are connected to enable cross-table calculations.

Using MDX

MDX is a functional query language that is part of the MDX specification. It's commonly used for querying multidimensional data structures, though it can also be used with tabular models.

Example MDX Query

This MDX query retrieves the sum of Sales Amount for the 'Accessories' product category in the current year:

SELECT {[Measures].[Sales Amount]} ON COLUMNS, [Product].[Category].&[Accessories] ON ROWS FROM [AdventureWorks] WHERE ([Date].[Calendar Year].&[2023])

Performance Considerations

Optimizing your queries is crucial for a good user experience. Consider the following:

  • Minimize Data Retrieval: Only select the columns and rows you need.
  • Efficient Calculations: Write performant DAX measures and avoid complex row-by-row calculations in calculated columns where possible.
  • Leverage Aggregations: Design your model to benefit from pre-aggregated data.
  • Understand Execution Plans: For complex queries, analyze execution plans to identify bottlenecks.
Tip: Use tools like DAX Studio to analyze query performance and identify areas for optimization.

Tools for Querying

Several tools can be used to connect to and query your Azure Analysis Services models:

  • SQL Server Management Studio (SSMS): A universal data management tool that supports connecting to and querying Analysis Services.
  • DAX Studio: A powerful free tool specifically designed for authoring, executing, and analyzing DAX queries.
  • Tabular Editor: An external tool for modeling and managing Analysis Services tabular models, which can also execute queries.
  • Power BI Desktop: When connecting to an Azure Analysis Services model as a data source, you can build reports and use DAX within Power BI.
  • Client Libraries: .NET, Java, and Python client libraries allow programmatic access to query your models.

Explore the Modeling Data section to learn how to design your models for optimal querying.