MSDN Documentation

Data Modeling in SQL Server Analysis Services

Data modeling is a crucial step in designing and building effective analytical solutions with SQL Server Analysis Services (SSAS). A well-designed data model provides a clear, intuitive, and performant foundation for business intelligence reporting and analysis.

Understanding Data Models

In SSAS, data models are structures that organize data from underlying data sources, making it accessible and understandable for end-users. The two primary types of models supported by SSAS are:

• Multidimensional Models: These models represent data in a multidimensional cube structure, allowing for complex analytical queries and aggregations across various dimensions.
• Tabular Models: These models use a relational in-memory database engine, offering a simpler and more intuitive data structure, often preferred for self-service BI and Power BI integration.

Key Concepts in Data Modeling

Dimensions

Dimensions provide context to your data. They are typically descriptive attributes that allow users to slice and dice measures. Examples include Time, Geography, Product, and Customer.

• Hierarchies: Within dimensions, you can define hierarchies to represent relationships, such as Year -> Quarter -> Month -> Day in a Time dimension.
• Attributes: Individual columns within a dimension table.

Measures

Measures are the quantitative values that users want to analyze. They are typically derived from numeric columns in your fact tables. Examples include Sales Amount, Quantity Sold, and Profit.

• Aggregation Functions: Measures are often aggregated using functions like Sum, Average, Count, Min, and Max.
• Calculated Measures: You can create custom measures using DAX (Data Analysis Expressions) or MDX (Multidimensional Expressions) to perform complex calculations.

Facts and Fact Tables

Fact tables are central to your data model and contain the numerical measures and foreign keys linking to dimension tables.

Relationships

Establishing correct relationships between fact and dimension tables is essential for the model to function correctly. In multidimensional models, these are often defined through

DIMENSION

and

MEASURE GROUP

definitions. In tabular models, these are standard relational foreign key relationships.

Designing for Performance

A well-designed data model not only simplifies analysis but also ensures optimal performance. Consider the following:

• Star Schema vs. Snowflake Schema: Understanding the trade-offs between denormalized star schemas and more normalized snowflake schemas.
• Data Types: Using appropriate data types for columns to reduce memory footprint and improve query speed.
• Partitioning: For large fact tables, partitioning can significantly improve query performance.
• Indexing (Tabular Models): Tabular models leverage columnar storage and indexing for high performance.

Tools for Data Modeling

SQL Server Data Tools (SSDT) is the primary development environment for creating and managing both Tabular and Multidimensional models in SSAS.

Best Practices

• Understand Business Requirements: Always start by thoroughly understanding the analytical needs of your users.
• Keep it Simple: Aim for the simplest model that meets requirements.
• Use Meaningful Names: Employ clear and descriptive names for dimensions, measures, and hierarchies.
• Document Your Model: Maintain documentation for your data model for future reference and collaboration.