Multidimensional Models in Analysis Services

This tutorial introduces the concept of multidimensional models within Microsoft SQL Server Analysis Services (SSAS). Multidimensional models are the cornerstone of OLAP (Online Analytical Processing) solutions, providing a powerful way to analyze large volumes of business data.

What are Multidimensional Models?

Multidimensional models represent data in a way that is intuitive for business users to query and analyze. Instead of traditional row-and-column tables, data is organized into cubes, which consist of:

Measures: Numerical values that can be aggregated, such as sales amount, quantity, or profit.
Dimensions: Hierarchical categories that provide context to the measures, such as Time, Geography, Product, or Customer.

These cubes allow for slice-and-dice, drill-down, and roll-up operations, enabling users to explore data from different perspectives.

Key Components of a Multidimensional Model

Cubes

A cube is the central object in a multidimensional model. It's a multidimensional data structure that aggregates measures along the attributes of dimensions. Cubes are typically built from relational data sources.

Dimensions

Dimensions provide the context for measures. They are typically represented as hierarchies, allowing users to navigate from a high-level summary to more granular details. For example, a 'Time' dimension might have hierarchies for Year, Quarter, Month, and Day.

Measures

Measures are the quantitative data that you want to analyze. They can be simple aggregations (e.g., SUM, COUNT) or more complex calculations using MDX (Multidimensional Expressions).

Hierarchies

Hierarchies define the structure of dimensions, enabling drill-down and roll-up capabilities. Users can explore data by moving up or down these hierarchies.

Building a Multidimensional Model

Developing a multidimensional model in SSAS involves several steps:

Data Source Configuration: Connect to your relational data warehouse or other data sources.
Cube Design: Define measures and dimensions that will form your cubes.
Dimension Creation: Build dimensions, define hierarchies, and configure attribute relationships.
Cube Processing: Process the cube to populate it with data from the data sources.
Client Application Integration: Connect to the deployed cube from client tools like Excel, Power BI, or custom applications.

MDX (Multidimensional Expressions)

MDX is the query language used to interact with multidimensional data in SSAS. It's similar in concept to SQL for relational databases but is designed for querying hierarchical and aggregated data structures.

Here's a simple MDX query example:

            
SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    {[Date].[Year].Members} ON ROWS
FROM
    [Adventure Works]
WHERE
    ([Product].[Category].&[Bikes], [Geography].[Country].&[United States])
            
        

Note: Multidimensional models are a mature and powerful OLAP technology. While Microsoft is increasingly focusing on Tabular models for newer BI scenarios, understanding multidimensional models is still crucial for many existing SSAS deployments and for certain complex analytical needs.