Designing Dimensions in Analysis Services

Dimensions are fundamental building blocks in a dimensional model for SQL Server Analysis Services (SSAS). They represent the "who, what, where, when, and how" of your business data, providing the context for your measures (facts). Designing effective dimensions is crucial for creating understandable, performant, and flexible business intelligence solutions.

Understanding Dimension Types

Analysis Services supports several types of dimensions, each with its own characteristics and use cases:

• Standard Dimensions: The most common type, representing hierarchical or flat structures.
• Junk Dimensions: Used to store low-cardinality, descriptive attributes that don't fit naturally into other dimensions, improving fact table performance.
• Degenerate Dimensions: Attributes that are stored directly in the fact table, typically transaction identifiers that don't require their own dimension table.
• Role-Playing Dimensions: A single dimension instance that can be used in multiple ways within a cube, allowing for different interpretations of the same data (e.g., 'Date' dimension used for 'Order Date', 'Ship Date', and 'Delivery Date').
• Measure Dimensions: A special dimension that groups measures together, useful for organizing and presenting a large number of measures.

Key Design Considerations

When designing dimensions, consider the following:

Hierarchy Design

Hierarchies are essential for enabling users to slice and dice data at different levels of granularity. Well-designed hierarchies are intuitive and reflect business understanding.

• Natural Hierarchies: Define hierarchies that naturally exist in your business data (e.g., Country > State > City).
• Parent-Child Hierarchies: Used for data where the number of levels is not fixed or known in advance (e.g., employee reporting structures, organizational charts).
• Ragged Hierarchies: Hierarchies where members at the same level may have different numbers of subordinate members or may terminate at different levels.

Attribute Design

Attributes are the individual columns within a dimension that describe members. Their design impacts usability and performance.

• Granularity: Ensure the lowest level of your dimension accurately represents a unique entity.
• Attribute Relationships: Define relationships between attributes to guide the query engine and improve performance. Key relationships include One-to-Many and Many-to-Many.
• Attribute Types: Assign appropriate attribute types (e.g., Regular, Key, Name, ID) for better usability and discoverability.
• User-Friendly Names: Use descriptive and understandable names for attributes.

Key Concepts

Familiarize yourself with these important concepts:

• Attribute Members: Individual items within an attribute (e.g., 'New York' is a member of the 'State' attribute).
• Attribute Relationships: Define how attributes relate to each other within a dimension.
• Hierarchies: Ordered sets of attributes that allow for drill-down and roll-up analysis.
• Levels: Stages within a hierarchy.

Example: Designing a 'Product' Dimension

Let's consider designing a 'Product' dimension:

1. Source Data: Identify the relevant columns from your source system (e.g., ProductID, ProductName, Category, Subcategory, Brand).
2. Attributes: Create attributes for each descriptive element: ProductID (as the key), ProductName, Category, Subcategory, Brand.
3. Hierarchy: Define a hierarchy such as 'Category' > 'Subcategory' > 'ProductName'.
4. Attribute Relationships: Ensure that 'ProductName' depends on 'Subcategory', and 'Subcategory' depends on 'Category'. This establishes a dependency path from the lowest attribute to the highest.
5. Granularity: The lowest level is typically the individual product.

Advanced Topics

As you gain experience, explore advanced dimension design techniques:

• Processing Options: Understand incremental and full processing for dimensions.
• Calculations: How to define calculated members within dimensions.
• Permissions: Securing access to dimension members.
• Dimension Writeback: Allowing users to modify dimension data directly.

Mastering dimension design is an iterative process. Regularly review your dimensions based on user feedback and evolving business needs to ensure your Analysis Services solution remains effective.