Understanding Dimensions and Measures in SQL Server Analysis Services (SSAS)
This article delves into the fundamental concepts of dimensions and measures within SQL Server Analysis Services (SSAS). These are the building blocks of any SSAS data model, providing the structure for slicing, dicing, and aggregating your business data.
What are Dimensions?
Dimensions provide the context for your data. They represent the business entities around which you analyze information. Think of them as the descriptive attributes that allow you to filter, group, and categorize your facts.
- Attributes: Each dimension is composed of attributes. For example, a 'Product' dimension might have attributes like 'Product Name', 'Category', 'Brand', and 'Color'.
- Hierarchies: Attributes can be organized into hierarchies, which represent natural relationships in your data. A common example is a 'Date' hierarchy with Year, Quarter, Month, and Day.
- Levels: Each step in a hierarchy is a level.
Example: Customer Dimension
Consider a 'Customer' dimension. Its attributes might include:
- Customer ID
- Customer Name
- Country
- State/Province
- City
You could create a 'Geography' hierarchy using Country > State/Province > City.
What are Measures?
Measures represent the quantitative values that you want to analyze. These are typically numeric values that can be aggregated (summed, averaged, counted, etc.). Measures are the 'facts' in your data model.
- Aggregation: Measures are designed to be aggregated. Common aggregation functions include SUM, COUNT, MIN, MAX, AVG.
- Fact Tables: Measures are usually derived from fact tables in a star or snowflake schema.
- Calculated Measures: SSAS allows you to create calculated measures using MDX (Multidimensional Expressions) or DAX (Data Analysis Expressions), enabling complex business logic and derived metrics.
Example: Sales Measures
In a sales cube, common measures include:
- Sales Amount
- Quantity Sold
- Discount Amount
- Profit
You might create a calculated measure for 'Profit Margin' as [Sales Amount] / [Quantity Sold] (with appropriate handling for division by zero).
The Relationship: Dimensions Slice and Dice Measures
The power of SSAS lies in the interaction between dimensions and measures. Dimensions allow you to slice and dice measures. For instance, you can analyze:
- Total Sales Amount by Product Category (using the 'Product' dimension)
- Quantity Sold by Year and Month (using the 'Date' dimension)
- Average Discount Amount by Customer Country (using the 'Customer' dimension)
Key SSAS Objects
In SSAS, you'll typically work with the following objects related to dimensions and measures:
- Dimensions: Created from source data (e.g., dimension tables).
- Attribute Hierarchies: Defined within dimensions.
- Measures: Created from fact tables or as calculated members.
- Measure Groups: A collection of related measures, often corresponding to a single fact table.
- Cubes: The primary data structure in SSAS, comprising dimensions and measure groups.
Conclusion
Dimensions and measures are the cornerstones of SSAS modeling. By mastering their definition and relationship, you can build robust and insightful analytical solutions that empower business users to explore and understand complex datasets.