MSDN Documentation

Design Guidance for Multidimensional Modeling in Analysis Services

This document provides comprehensive design guidance for creating effective and efficient multidimensional models in Microsoft SQL Server Analysis Services (SSAS). Proper modeling is crucial for delivering performant and user-friendly business intelligence solutions.

1. Understanding Your Business Requirements

Before diving into technical details, it's essential to have a clear understanding of the business domain and the questions users need to answer. This involves:

• Identifying key performance indicators (KPIs).
• Defining business processes and their relationships.
• Understanding the granularity of data required.
• Collaborating with business stakeholders and end-users.

2. Core Concepts of Multidimensional Modeling

Multidimensional models are built around the concepts of:

Dimensions

Dimensions represent the context in which data is analyzed. They provide descriptive attributes for measures. Examples include Time, Geography, Product, and Customer.

• Hierarchies: Organize dimension attributes into logical structures (e.g., Year -> Quarter -> Month -> Day).
• Attributes: Individual properties within a dimension (e.g., City, State, Country).
• Uniqueness: Ensure unique keys for dimension members.

Measures

Measures represent quantifiable business metrics that users want to analyze. They are typically numeric values aggregated from fact tables.

• Fact Tables: Contain the measures and foreign keys to dimension tables.
• Aggregation: Define how measures are aggregated (e.g., SUM, COUNT, AVERAGE).
• Measure Groups: Organize related measures within a cube.

Cubes

A cube is a collection of measure groups and dimensions that define a specific analytical subject area.

3. Best Practices for Dimension Design

Well-designed dimensions are fundamental to a usable and performant cube.

• Snowflake vs. Star Schema: While star schemas are generally preferred for simplicity and performance, snowflake schemas can be useful for complex dimension relationships or when reducing redundancy is paramount.
• Attribute Relationships: Define attribute relationships to leverage SSAS's processing and query optimization. This ensures that SSAS understands how attributes relate to each other within a hierarchy.
• User-Friendly Names: Use clear and intuitive names for dimensions, attributes, and hierarchies.
• Degenerate Dimensions: Include useful transaction identifiers (e.g., Invoice Number) directly in the fact table if they serve analytical purposes and don't warrant a separate dimension.
• Date Dimensions: Create dedicated date dimensions with comprehensive hierarchies (Year, Quarter, Month, Day, Weekday, Fiscal Periods) for robust time-based analysis.

4. Best Practices for Measure Design

Effective measure design ensures data accuracy and usability.

• Atomic Measures: Model measures at their lowest logical level of granularity.
• Calculated Measures: Use SSAS MDX expressions for complex calculations rather than trying to pre-calculate everything in the data warehouse.
• Measure Formatting: Define appropriate data types and display formats for measures.
• Related Measure Groups: Place measures in the appropriate measure group based on their fact table and granularity.
• Semi-Additive Measures: Understand and correctly implement semi-additive measures (e.g., inventory levels) that can be aggregated across dimensions like time but not others.

5. Performance Considerations

Optimizing performance is key to a responsive BI solution.

• Aggregations: Design and deploy aggregations strategically to speed up query performance.
• Partitioning: Partition large fact tables to improve query performance and manageability.
• Indexing: Ensure appropriate indexing on underlying relational source tables.
• Data Types: Use efficient data types in the source system.
• Server Configuration: Tune SSAS server properties based on workload and hardware.

6. Security and Deployment

Implementing robust security and a streamlined deployment process is vital.

• Row-Level Security: Implement row-level security to restrict access to specific data based on user roles.
• Dimension-Level Security: Control access to specific dimension members.
• Deployment Scenarios: Understand deployment options, including deploying to SSAS in Integrated mode or Tabular mode (though this document focuses on multidimensional).
• Source Control: Use source control for SSAS project files.

Further Reading:

• MDX Syntax Guide
• Analysis Services Tutorials
• Data Warehousing Concepts