SQL Server Analysis Services Multidimensional Modeling

Understanding Dimensions

In a multidimensional model, dimensions represent the business perspectives or attributes by which users want to analyze data. Common examples include Time, Geography, Product, and Customer. Dimensions provide context to measures.

Key Characteristics of a Dimension:

• Hierarchies: Dimensions often contain one or more hierarchies, allowing users to drill down and roll up data across different levels of granularity (e.g., Year > Quarter > Month > Day).
• Attributes: These are the individual columns within a dimension table that describe the dimension member.
• Members: The actual data instances within an attribute (e.g., '2023' for the Year attribute, 'USA' for the Country attribute).
• Uniqueness: Dimension keys should be unique to ensure correct aggregation.

Types of Dimensions

SSAS supports several types of dimensions, each suited for different modeling scenarios:

Standard Dimensions:

The most common type, representing typical business attributes. They can have hierarchies and are generally straightforward to design.

Degenerate Dimensions:

Attributes that are part of a fact table but do not have a corresponding dimension table. Often used for transaction identifiers or status codes.

Role-Playing Dimensions:

A single physical dimension can be referenced multiple times in a cube with different roles. The most common example is a Date dimension, which can serve as an 'Order Date', 'Ship Date', and 'Delivery Date'.

Junk Dimensions:

Used to consolidate low-cardinality, miscellaneous attributes from a fact table that don't warrant their own individual dimensions. This helps to avoid "fact table snowflaking."

Parent-Child Dimensions:

Designed for hierarchical data where the relationship between members is irregular or dynamic, such as organizational charts or account structures. Each member can have a parent, and members can have varying numbers of children.

Designing Dimension Tables

The source for your SSAS dimensions is typically a dimensional model in your data warehouse. Best practices for dimension tables include:

• Surrogate Keys: Always use surrogate keys (system-generated, integer keys) for dimension tables. This decouples the SSAS model from source system changes and improves performance.
• Natural Keys: Include the original business key from the source system for reference and traceability.
• Descriptive Attributes: Include meaningful attributes that users will want to use for filtering and grouping.
• Slowly Changing Dimensions (SCDs): Implement appropriate SCD types (Type 1, Type 2, Type 3, etc.) to track historical changes in dimension attributes. Type 2 is most common for historical analysis.

Creating Dimensions in SSAS Tabular Model

In SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects, you create dimensions via the Dimension Designer.

1. Add New Dimension: Right-click on the 'Dimensions' folder in Solution Explorer and select 'Add Dimension'.
2. Choose Data Source: Select the data source view that contains your dimension tables.
3. Select Tables: Choose the primary dimension table and any related tables for attributes.
4. Define Hierarchies: Drag attributes to the 'Hierarchy Variations' pane to create hierarchies.
5. Configure Attributes: Set attribute properties, such as Key Columns, Name Column, and Attribute Hierarchy Optimization.
6. Set Dimension Properties: Configure overall dimension properties, including its type.

Dimension Properties and Optimization

Key properties to consider during dimension design:

• Attribute Hierarchy Enabled: Set to True for attributes that will be part of a hierarchy or used for analysis.
• Attribute Hierarchy Optimization: Set to 'NotOptimized' if the attribute is not used in a hierarchy or for queries. For frequently queried attributes, 'FullyOptimized' is recommended.
• AttributeIsAggregatable: Usually set to True for attributes that can be aggregated (e.g., City can roll up to State).
• IsDaylightSavingTime, IsWeekday, etc.: For Time dimensions, these properties are invaluable for time-based analysis.
• Unknown Member: Configure how unknown or null values are handled.

Advanced Dimension Concepts

Natural Hierarchies vs. User-Defined Hierarchies:

Natural hierarchies are derived from the relationships in your source data (e.g., Country > State > City). User-defined hierarchies offer more flexibility in structuring how users navigate data.

Dimension Relationships:

Define relationships between dimensions when they share common attributes, which can improve query performance and enable complex analyses.

Using Dimension Security:

Apply security at the dimension or attribute level to restrict access to specific members or data. This is crucial for compliance and data governance.