Managing Dimensions

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

Understanding Dimension Concepts

A dimension is typically based on a fact table's foreign key, representing an attribute of the business. Common examples include:

Time: Year, Quarter, Month, Day
Geography: Country, State, City
Product: Category, Subcategory, Product Name
Customer: Segment, Name, Demographics

Each dimension contains attributes, which represent the various levels of detail within that dimension. For instance, the 'Time' dimension might have attributes like 'Year', 'Month Name', and 'Day'.

Creating and Configuring Dimensions

Dimensions are created in your SSAS project using tools like SQL Server Data Tools (SSDT). You can define dimensions based on existing tables in your data source view.

Key configurations for a dimension include:

Attribute Relationships: Define how attributes relate to each other within the dimension, forming hierarchies.
Key Columns: Specify the primary key column(s) from the source table that uniquely identify each member of the dimension.
Name Column: Define the column that provides the descriptive name for each member.
Attribute Properties: Configure properties like AttributeHierarchyEnabled, AttributeHierarchyVisible, and AttributeHierarchyOrder to control how attributes appear and are sorted in the cube.

Hierarchies

Hierarchies represent the natural drill-down paths within a dimension. For example, in a 'Geography' dimension, you might have a hierarchy of 'Country' -> 'State' -> 'City'. Users can then navigate through these levels to explore data at different granularities.

Hierarchies can be:

User-defined: Created manually by dragging and dropping attributes in SSDT.
Natural: Automatically generated based on defined attribute relationships.

Dimension Types

SSAS supports several dimension types, each offering different ways to manage and present dimension data:

Standard Dimension: The most common type, typically based on a single table.
Degenerate Dimension: An attribute that resides directly in a fact table but is treated as a dimension (e.g., an invoice number).
Scattered Dimension: Used when dimension attributes are spread across multiple tables, often requiring a view or a union to consolidate.
Parent-Child Dimension: Represents hierarchical data where the parent-child relationship is stored within a single table (e.g., organizational structures).
Role-Playing Dimension: A single physical dimension that can be used multiple times in a cube with different roles (e.g., a 'Date' dimension used for 'Order Date', 'Ship Date', and 'Delivery Date').

Tip: Carefully consider your reporting requirements and data structure when choosing the appropriate dimension type.

Best Practices for Dimension Design

Keep dimensions lean: Avoid including too many attributes that are rarely used for analysis.
Use descriptive names: Ensure attribute and hierarchy names are intuitive for end-users.
Enable caching: Configure dimension caching to improve query performance.
Order attributes correctly: Ensure hierarchies are structured logically for drill-down.
Consider translations: If your solution needs to support multiple languages, configure dimension translations.
Use surrogate keys: For slowly changing dimensions, surrogate keys help manage historical data effectively.

Managing dimensions effectively is a cornerstone of building robust and insightful multidimensional models in SQL Server Analysis Services. By understanding the concepts and leveraging the available tools and configurations, you can empower your users to gain deeper insights from their data.

For more detailed information, refer to the following topics: