Querying Data with SQL Server Analysis Services

This section provides comprehensive guidance on how to query data from SQL Server Analysis Services (SSAS) multidimensional and tabular models. Understanding the various query languages and tools available is crucial for extracting meaningful insights from your business intelligence solutions.

Understanding Query Languages

SSAS supports two primary query languages, depending on the type of model you are working with:

Multidimensional Expressions (MDX): Used for querying multidimensional cubes. MDX is a powerful, specialized language designed for slicing, dicing, and aggregating data in OLAP cubes.
Data Analysis Expressions (DAX): Used for querying tabular models. DAX is a formula expression language commonly used in Power BI, SSAS tabular models, and Power Pivot in Excel.

Querying Multidimensional Models (MDX)

Introduction to MDX

MDX allows you to navigate and retrieve data from the hierarchical structures of OLAP cubes. It provides extensive capabilities for complex analytical queries.

Common MDX Statements and Functions

SELECT: The core statement for retrieving data.
FROM: Specifies the cube from which to retrieve data.
WHERE: Filters the context of the query.
WITH MEMBER: Defines calculated members.
AGGREGATE(), SUM(), AVG(): Aggregation functions.
HIERARCHIZE(), DRILLDOWN(), DRILLUP(): Functions for navigating hierarchies.

Example MDX Query

SELECT
    {[Measures].[Sales Amount], [Measures].[Profit]} ON COLUMNS,
    {[Product].[Category].[Category].MEMBERS} ON ROWS
FROM
    [Adventure Works DW]
WHERE
    ([Date].[Calendar Year].[CY 2019], [Customer].[Country].[United States])

Tools for MDX Querying

SQL Server Management Studio (SSMS): Connect to your SSAS instance and use the MDX Query Editor.
SQL Server Data Tools (SSDT): For developing and testing MDX queries during model development.
Client Applications: Applications like Power BI, Excel, and custom applications can send MDX queries to SSAS.

Tip: For complex MDX queries, start with simpler statements and gradually add complexity. Utilize the SSMS IntelliSense for assistance.

Querying Tabular Models (DAX)

Introduction to DAX

DAX is a functional language that enables you to define calculations and analyze data in tabular models. Its syntax is similar to Excel formulas, making it more accessible for many users.

Common DAX Functions and Concepts

CALCULATE(): Modifies the filter context in which an expression is evaluated.
FILTER(): Returns a table that has been filtered.
SUM(), AVERAGE(): Aggregation functions.
RELATED(), RELATEDTABLE(): Functions for navigating relationships.
Measures: Calculated values that respond to user interactions.
Calculated Columns: Columns that are computed at data refresh time.

Example DAX Query (using Tabular Editor or DAX Studio)

EVALUATE
SUMMARIZECOLUMNS (
    'Product'[Category],
    'Date'[CalendarYear],
    "Total Sales", SUM ( 'Sales'[SalesAmount] ),
    "Total Profit", SUM ( 'Sales'[Profit] )
)
WHERE
    'Date'[CalendarYear] = 2019 AND 'Customer'[Country] = "United States"

Tools for DAX Querying

DAX Studio: A powerful and indispensable tool for writing, executing, and analyzing DAX queries.
Tabular Editor: A popular external tool for developing and managing SSAS tabular models, including writing DAX.
SQL Server Management Studio (SSMS): Can be used to query SSAS tabular models with DAX.
Power BI Desktop: DAX is the primary language for creating measures and calculated columns within Power BI.

Note: DAX operates on tables and columns, much like a relational database, but with powerful time-intelligence and context-aware functions.

Best Practices for Querying

Understand Your Model: Familiarize yourself with the structure of your cubes or tabular models, including dimensions, hierarchies, and measures.
Use Appropriate Tools: Choose the right tools for development, testing, and performance tuning.
Optimize Queries: Write efficient queries to ensure fast response times. Avoid unnecessary complexity and redundant calculations.
Leverage Aggregations: Ensure that your models are designed with appropriate aggregations to speed up query performance.
Test Thoroughly: Validate your query results against known data or expected outcomes.

By mastering MDX and DAX, you can unlock the full potential of your SQL Server Analysis Services data and provide powerful analytical capabilities to your users.