SQL Server Analysis Services

Best Practices for Dimension Design

Effective dimension design is crucial for building performant and user-friendly Analysis Services cubes. This guide outlines best practices to ensure your dimensions are robust, scalable, and intuitive for end-users.

1. Understand Your Business Requirements

Before designing any dimension, thoroughly understand:

• Business Questions: What insights do users need from the data?
• Reporting Needs: How will users slice and dice the data?
• Data Granularity: What is the lowest level of detail for each attribute?

2. Dimension Types and Usage

Choose the appropriate dimension type for your needs:

• Standard Dimensions: The most common type, representing hierarchical or flat attributes.
• Role-Playing Dimensions: Use a single physical dimension for multiple logical roles (e.g., Date dimension for Order Date, Ship Date, etc.).
• Degenerate Dimensions: Attributes that don't have their own dimension table but are included directly in the fact table (e.g., Invoice Number).
• Fact Dimensions: Less common, used for very specific scenarios where attributes of a fact are dimensionalized.

3. Hierarchies: Structure and Design

Hierarchies provide a natural way for users to navigate data. Design them carefully:

• Logical Flow: Ensure hierarchies reflect real-world relationships (e.g., Country -> State -> City).
• User-Defined Hierarchies: Allow users to create their own hierarchies.
• Parent-Child Hierarchies: Use sparingly for self-referencing relationships (e.g., Employee reporting to Manager). Be aware of performance implications.
• Natural Hierarchies: Typically represented by a single attribute or a series of related attributes.
• Avoid Overlapping Hierarchies: Can lead to confusion and incorrect aggregations.

4. Attribute Design Considerations

Attributes are the building blocks of dimensions. Pay attention to:

• Granularity: Each row in a dimension table should represent a unique business entity (e.g., a specific product, a specific customer).
• Attributes of Attributes: Avoid "attributes of attributes" by flattening hierarchies where appropriate, or using attribute relationships.
• Descriptive Attributes: Include attributes that users will need for filtering, grouping, and labeling (e.g., Product Name, Customer City, Employee Department).
• Key Attributes: Each attribute should have a unique key.
• Attribute Relationships: Define relationships between attributes to enable proper aggregation and slicing (e.g., a City attribute relates to a State attribute).

5. Performance Optimization

Several techniques can improve dimension performance:

• Proactive Caching: Configure appropriate caching settings for dimensions.
• Dimension Table Size: Keep dimension tables as lean as possible while retaining necessary attributes.
• Indexing: Ensure proper indexing on dimension tables in the underlying data source.
• Attribute Relationships: Properly defined attribute relationships are critical for query performance.
• Aggregation Design: While not strictly dimension design, well-designed dimensions facilitate better aggregation strategies.

6. Handling Slowly Changing Dimensions (SCDs)

Decide how to handle changes in dimension data over time:

• Type 0: Retain Original: No changes are applied.
• Type 1: Overwrite: New values overwrite old values.
• Type 2: Add New Row: A new row is added for each change, preserving history. This is the most common for historical analysis.
• Type 3: Add New Attribute: A new attribute column is added to store the previous value.
• Type 4: Historical Audit Table: A separate table tracks changes.

Choose the SCD type that best suits the business requirements for tracking historical data.

7. Naming Conventions and User Experience

Consistency is key:

• Clear and Concise Names: Use descriptive names for dimensions and attributes.
• Consistent Formatting: Apply uniform naming conventions across all dimensions.
• User-Friendly Labels: Provide business-friendly names for attributes that users will see in BI tools.

8. Example: Product Dimension Design

A typical Product dimension might include:

• Dimension Attributes:
  • ProductKey (Surrogate Key)
  • ProductID (Natural Key)
  • ProductName
  • ProductDescription
  • Brand
  • Category
  • Subcategory
  • Color
  • Size
  • UnitPrice
  • ValidFromDate (for SCD Type 2)
  • ValidToDate (for SCD Type 2)
  • IsCurrent (for SCD Type 2)
• Hierarchies:
  • Category -> Subcategory -> Brand -> ProductName
• Attribute Relationships:
  • ProductName -> Subcategory
  • Subcategory -> Category

By adhering to these best practices, you can create robust and efficient multidimensional models in SQL Server Analysis Services.