Introduction to Multidimensional Models in Analysis Services

This document provides a foundational understanding of multidimensional models within Microsoft SQL Server Analysis Services (SSAS). Multidimensional models are the traditional and most mature data modeling approach in Analysis Services, designed for complex analytical querying and reporting.

What are Multidimensional Models?

Multidimensional models represent data in a multidimensional structure, often referred to as a data cube. This structure allows for efficient querying and analysis of large volumes of business data from various perspectives. Key components of a multidimensional model include:

• Dimensions: These represent the business perspectives or entities through which data is analyzed. Examples include Time, Geography, Products, and Customers. Dimensions are typically hierarchical.
• Measures: These are the quantitative values that users want to analyze, such as Sales Amount, Quantity Sold, or Profit. Measures are stored in a fact table and are aggregated within dimensions.
• Cubes: A cube is a structured way to present measures and dimensions, allowing users to slice and dice data across different dimensions. A cube is essentially a data structure built from fact tables (containing measures) and related dimension tables.

Key Concepts

Dimensions and Hierarchies

Dimensions provide context to measures. They are organized into hierarchies, which allow users to drill down from a high-level view (e.g., Year) to more granular levels (e.g., Quarter, Month, Day). This hierarchical structure is fundamental to multidimensional analysis.

For example, a 'Geography' dimension might have a hierarchy like:

• Country
• State/Province
• City

Measures and Aggregations

Measures represent the core business metrics you want to analyze. Analysis Services pre-calculates aggregations for measures at various levels of the dimension hierarchies. This significantly speeds up query performance, allowing users to get insights quickly.

Common aggregation functions for measures include:

• Sum
• Count
• Average
• Min
• Max

Benefits of Multidimensional Models

• Performance: Optimized for fast querying of large datasets through pre-aggregated data.
• Rich Functionality: Supports complex business logic, calculations, and advanced analytical features.
• User Familiarity: The cube metaphor is intuitive for many business users familiar with traditional OLAP concepts.
• Scalability: Can handle very large data volumes effectively.

Common Use Cases

Multidimensional models are ideal for scenarios requiring:

• Complex financial reporting and budgeting.
• Sales performance analysis with intricate drill-down capabilities.
• Supply chain and inventory management analytics.
• Customer segmentation and behavioral analysis.

Getting Started

To work with multidimensional models, you will typically use:

• SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects for designing and developing your models.
• SQL Server Management Studio (SSMS) for managing and deploying your Analysis Services instances and databases.
• MDX (Multidimensional Expressions) query language for retrieving data from the cubes.