Understanding Cubes in SQL Server Analysis Services

Cubes are fundamental to the multidimensional model in SQL Server Analysis Services (SSAS). They provide a multidimensional view of data, allowing for fast and efficient analysis of large datasets. A cube is essentially a data structure that organizes business information to support rapid querying and data analysis.

Key Concepts of Cubes

• Dimensions: These represent the perspectives by which you want to analyze data. Common examples include Time, Geography, Product, and Customer. Each dimension contains hierarchies that allow users to drill down and roll up data.
• Measures: These are the numerical values that you want to analyze, such as Sales Amount, Quantity, or Profit. Measures are typically aggregated within the cube.
• Facts: The underlying data that measures are derived from, usually stored in fact tables in a data warehouse.
• Hierarchies: Structures within dimensions that allow for aggregation and drill-down capabilities (e.g., Year -> Quarter -> Month -> Day in a Time dimension).
• Members: The individual items within a hierarchy (e.g., "2023" for the Year level, "North America" for a Country level).

Designing a Cube

The design of a cube is critical for performance and usability. A well-designed cube should:

• Align with business reporting requirements.
• Leverage appropriate aggregation strategies.
• Utilize effective dimension modeling.

Steps in Cube Design:

1. Identify Business Requirements: Understand what questions users need to answer.
2. Define Measures: Determine the key performance indicators (KPIs) to be included.
3. Define Dimensions: Identify the business perspectives for analysis.
4. Model Hierarchies: Structure dimensions for effective drill-down and roll-up.
5. Configure Aggregations: Design pre-calculated aggregations to optimize query performance.
6. Process the Cube: Load data into the cube structure.

Cube Types

SQL Server Analysis Services supports two primary data modeling approaches:

• Multidimensional Models: The traditional approach, built around cubes, dimensions, and measures. Offers rich functionality and flexibility.
• Tabular Models: A newer, in-memory columnar database approach. Often simpler to develop and can offer superior performance for certain workloads, especially with large datasets.

This documentation focuses on the Multidimensional Model and its core component: the Cube.

MDX (Multidimensional Expressions)

Cubes are queried using MDX, a powerful query language for OLAP data. MDX allows for complex analysis, slicing, dicing, and drilling operations.

-- Example MDX Query to get total sales by product category for the year 2023
SELECT
    {[Measures].[Internet Sales Amount]} ON COLUMNS,
    [Product].[Category].[Category].MEMBERS ON ROWS
FROM
    [Adventure Works DW2019]
WHERE
    ([Date].[Calendar Year].&[2023])
        

Further Reading

• Understanding Dimensions
• Working with Measures
• Processing Analysis Services Objects
• MDX Syntax Overview