Dimension Design in SQL Server Analysis Services

Dimensions are fundamental objects in dimensional modeling for SQL Server Analysis Services (SSAS). They provide the context for your data, allowing users to slice, dice, and analyze facts (measures) from various perspectives. Effective dimension design is crucial for building a performant and user-friendly data model.

Introduction to Dimensions

In SSAS, a dimension is a collection of attributes that describe the business entities you want to analyze. For example, a 'Product' dimension might contain attributes like 'Product Name', 'Category', 'Subcategory', 'Brand', and 'Color'. These attributes can be used to filter or group measures in your cubes.

Dimensions are typically sourced from dimension tables in your relational data warehouse. SSAS supports various types of dimensions, each with its own characteristics and use cases.

Types of Dimensions

Analysis Services supports several types of dimensions:

Standard Dimensions: The most common type, representing unchanging descriptive attributes.
Role-Playing Dimensions: A single dimension (e.g., 'Date') can be reused multiple times in a cube, each with a different role (e.g., 'Order Date', 'Ship Date', 'Delivery Date').
Junk Dimensions: Used to group together low-cardinality, miscellaneous attributes that don't warrant their own dimensions.
Degenerate Dimensions: Attributes that exist in a fact table but not in a corresponding dimension table.
Fact Dimensions: A special type of dimension used to hold high-cardinality attributes.
In-Active Dimensions: Dimensions that are not currently being used but are retained for potential future use.

Attribute Design

Attributes are the building blocks of dimensions. When designing attributes, consider the following:

Attribute Hierarchy: Define how attributes relate to each other to form hierarchies.
Key Attribute: Select a unique attribute to uniquely identify each member of the dimension.
Attribute Relationships: Define relationships between attributes to optimize query performance and enable attribute-based hierarchies.
Data Types: Ensure attributes have appropriate data types.
Key Columns: Map attribute columns to the corresponding columns in your source data.

Hierarchy Design

Hierarchies allow users to navigate through different levels of granularity within a dimension. Common examples include 'Geography' (Country -> State -> City) or 'Time' (Year -> Quarter -> Month -> Day).

Types of hierarchies:

User-Defined Hierarchies: Manually created hierarchies by linking attributes.
Parent-Child Hierarchies: Used for data with a natural parent-child relationship, such as organizational structures or account trees.

Best Practice: Design hierarchies to reflect common business analysis paths. Avoid excessively deep or complex hierarchies that can hinder usability.

Advanced Dimension Features

Analysis Services offers advanced features for dimension design:

Attribute Transposition: Optimizes storage and query performance for specific attribute patterns.
Natural Hierarchies: Automatically generated hierarchies based on attribute relationships.
Calculated Members: Create calculated members within hierarchies to perform on-the-fly calculations.
Dimension Security: Implement security at the dimension member level to restrict user access.

Tip: Regularly review and refine your dimension designs based on user feedback and evolving business requirements.

For detailed information on specific configurations and best practices, refer to the official Microsoft documentation.

Last Updated: October 26, 2023