Designing Effective SSAS Dimensions

Effective dimension design is crucial for building a robust and performant SQL Server Analysis Services (SSAS) multidimensional model. Dimensions provide the business context for your data, allowing users to slice and dice measures in meaningful ways. This article explores key principles and best practices for designing SSAS dimensions.

Understanding Dimension Types

SSAS supports several dimension types, each with its own characteristics:

• Standard Dimensions: The most common type, representing entities like Customers, Products, Dates, etc.
• Degenerate Dimensions: Attributes that don't have a corresponding fact table row but are directly derived from the fact table itself (e.g., Invoice Number in a sales fact table).
• Role-Playing Dimensions: A single dimension used in multiple contexts within a cube (e.g., a Date dimension used for Order Date, Ship Date, and Delivery Date).
• Junk Dimensions: A dimension that consolidates low-cardinality flags and indicators from a fact table into a single dimension to avoid cluttering the fact table.
• Factless Fact Tables: Used to model events or relationships where there are no measurable facts (e.g., tracking student attendance).

Key Design Principles

When designing your dimensions, consider the following:

1. Business Understanding

Deeply understand the business requirements. What questions do users need to answer? How do they typically analyze data? This understanding will guide your attribute selection and hierarchy design.

2. Granularity

Define the lowest level of detail for each dimension. For example, a 'Product' dimension might have attributes like Product Key, Product Name, Category, Subcategory. The granularity should match the lowest level of detail in your fact tables.

3. Attributes

Attributes are the descriptive characteristics of your dimension members. They should be:

• Atomic: Each attribute should represent a single piece of information. Avoid combining multiple distinct attributes into one.
• Descriptive: Provide meaningful labels and values that users can easily understand.
• Consistent: Ensure data quality and consistency across all dimension members.

4. Hierarchies

Hierarchies allow users to navigate data at different levels of aggregation. Common hierarchies include Time (Year > Quarter > Month > Day) and Geography (Country > State > City). Design hierarchies that reflect natural business relationships and reporting needs.

• Parent-Child Hierarchies: Useful for organizational structures or recursive relationships (e.g., Employee reporting to Manager).
• Unbalanced Hierarchies: Hierarchies where different branches have different depths (e.g., product categories).

5. Snowflake vs. Star Schema

While SSAS is optimized for star schemas, understanding snowflake schemas is also important. In a star schema, dimensions are denormalized. In a snowflake schema, dimension tables are normalized, leading to more tables but potentially less redundancy. For SSAS, a denormalized star schema is generally preferred for performance.

6. Handling Slowly Changing Dimensions (SCDs)

SCDs are dimensions where attribute values can change over time. SSAS offers several types of SCD handling:

• Type 0 (Fixed): The attribute never changes.
• Type 1 (Overwrite): The new value replaces the old value. History is lost.
• Type 2 (Add New Row): A new row is added for the new value, with effective start and end dates. This preserves history.
• Type 3 (Add New Column): A new column is added to store the "previous" value. Limited history.

Choosing the right SCD type depends on the business requirement for historical tracking.

Technical Considerations

1. Dimension Properties

Configure dimension properties carefully. Key properties include:

• Attribute Order: Affects how attributes are displayed in client tools.
• AttributeHierarchyEnabled: Determines if an attribute can be used in a hierarchy.
• AttributeHierarchyVisible: Determines if an attribute is visible to users.
• IsAggregatable: Controls whether an attribute can be aggregated.
• Key Columns: The column(s) that uniquely identify a member.
• Name Column: The column that holds the member's name.

2. Cube Performance

Dimension design significantly impacts cube performance:

• Cardinality: High cardinality attributes (many unique values) can negatively impact performance. Consider breaking them into separate dimensions or using aggregation designs.
• Attribute Relationships: Define attribute relationships to inform the query engine about relationships between attributes within a dimension, enabling better aggregation.

3. Deployment Considerations

Understand how your dimensions will be deployed and processed. Efficient processing strategies are key to keeping your data warehouse and SSAS cubes up-to-date.

Example Scenario: Customer Dimension

Let's consider a 'Customer' dimension. A good design might include:

• CustomerKey (Surrogate Key): Unique identifier for each customer record.
• CustomerBK (Business Key): The original primary key from the source system.
• Customer Name: Full name of the customer.
• Email Address: Customer's email.
• City, State, Country: Geographic attributes for analysis.
• Customer Segment: (e.g., Premium, Standard).
• Date of Birth: For age-based analysis.
• Account Creation Date: For tenure analysis.

We could create hierarchies like Country > State > City and potentially a hierarchy based on Customer Segment.

Conclusion

Designing effective SSAS dimensions is an iterative process that requires a blend of business understanding and technical expertise. By adhering to best practices for attribute design, hierarchy creation, and SCD handling, you can build SSAS models that are both performant and user-friendly, enabling powerful business intelligence insights.