Multidimensional Models in SQL Server Analysis Services

Multidimensional models provide a robust and flexible framework for designing analytical solutions. They are built around cubes, which are multidimensional data structures that allow users to analyze data from different perspectives (dimensions) using predefined calculations (measures).

Key Concepts

Cubes

A cube is the central object in a multidimensional model. It aggregates data from fact tables and organizes it into dimensions. Cubes enable fast querying and analysis of large datasets.

Dimensions

Dimensions represent the business perspectives through which users analyze data. Common dimensions include Time, Geography, Products, and Customers. Dimensions can have hierarchies, allowing users to drill down or roll up data.

• Attributes: Members within a dimension (e.g., Year, Month, Day for a Time dimension).
• Hierarchies: Ordered sets of attributes that define levels of aggregation (e.g., Country > State > City).

Measures

Measures represent the quantitative data that users want to analyze. They are typically derived from the measures column in a fact table and can be aggregated using various functions (e.g., Sum, Count, Average).

• Calculated Measures: Measures defined using MDX (Multidimensional Expressions) to perform complex calculations.
• KPIs (Key Performance Indicators): Specific measures defined to track business performance against goals.

Fact Tables

Fact tables store the transactional data or events that are being analyzed. They typically contain foreign keys that link to dimension tables and one or more measures.

Dimension Tables

Dimension tables store descriptive attributes about business entities. They are linked to the fact table through foreign keys.

Building a Multidimensional Model

The process of building a multidimensional model in SQL Server Analysis Services (SSAS) typically involves the following steps:

1. Define Data Sources: Connect to the underlying relational databases or other data sources containing your business data.
2. Create Data Source Views: Define a logical view of your data sources, often involving joining tables and creating calculated columns.
3. Design the Cube:
• Define measures based on fact table columns.
• Define dimensions and their hierarchies.
• Establish relationships between dimensions and the cube.
4. Configure and Process the Cube: Set properties, define aggregations, and process the cube to populate it with data.
5. Deploy and Query: Deploy the cube to a SSAS server and query it using MDX or other client tools.

MDX (Multidimensional Expressions)

MDX is the query language used to retrieve data from multidimensional models. It is a powerful and expressive language for slicing, dicing, and aggregating data. You can use MDX to create complex calculations, perform time-based analysis, and much more.

Example MDX query to retrieve total sales for the year 2023:

SELECT
  {[Measures].[Internet Sales Amount]} ON COLUMNS,
  {[Date].[Calendar Year].&[2023]} ON ROWS
FROM [Adventure Works DW]
        

Benefits of Multidimensional Models

• Performance: Pre-aggregated data structures allow for very fast query responses, especially on large datasets.
• Rich Functionality: Supports complex calculations, what-if analysis, and business logic through MDX.
• User Familiarity: Many business users are familiar with the concept of cubes and dimensions from traditional OLAP tools.
• Scalability: Can handle very large amounts of data and complex analytical requirements.

Considerations

• Development Complexity: Designing and maintaining complex multidimensional models can be challenging.
• Maintenance: Schema changes can sometimes be more involved than with tabular models.
• Tooling: While Visual Studio provides excellent tools, some advanced scenarios might require deeper MDX knowledge.

For more detailed information on specific aspects of multidimensional modeling, please refer to the related topics in the documentation.