Dimensions

What are Dimensions?

In a multidimensional database, dimensions represent the perspectives by which you analyze data. They provide the context for the measures within a cube. For example, in a sales cube, common dimensions would include:

• Time: Analyzing sales by day, week, month, year, or quarter.
• Geography: Analyzing sales by country, state, city, or store.
• Product: Analyzing sales by product category, subcategory, or individual product.
• Customer: Analyzing sales by customer segment, age group, or individual customer.

Each dimension consists of hierarchies, which allow users to navigate data at different levels of granularity.

Key Concepts of Dimensions

• Attributes: The basic building blocks of a dimension. Each attribute represents a characteristic or property of the dimension (e.g., 'Year' or 'Month' in a Time dimension).
• Hierarchies: A path from a higher-level attribute to a lower-level attribute within a dimension. This enables drill-down and roll-up operations.
• Levels: The distinct layers within a hierarchy. For example, a Time hierarchy might have levels for Year, Quarter, Month, and Day.
• Members: The individual values within a dimension attribute or level. For instance, '2023' would be a member of the 'Year' attribute.
• Parent-Child Hierarchies: A special type of hierarchy where relationships are defined directly between parent and child members (e.g., employee-manager relationships).

Creating and Managing Dimensions

Dimensions are typically created and managed using SQL Server Data Tools (SSDT) for Visual Studio. Here's a general workflow:

1. Design the Dimension: Define the attributes and their relationships.
2. Define Hierarchies: Create user-defined hierarchies to enable slice-and-dice analysis.
3. Configure Attributes: Set attribute properties, such as key columns, name columns, and attribute types (e.g., Regular, Skipped, Unbalanced, Parent-child).
4. Process the Dimension: Load the dimension data from the data source into the Analysis Services database.

Dimension Types

Analysis Services supports several types of dimensions:

• Standard Dimensions: The most common type, typically used for descriptive attributes like Time, Geography, and Product.
• Snowflake Dimensions: Dimensions where attributes are normalized into multiple related tables.
• Slowly Changing Dimensions (SCDs): Dimensions that track historical changes to attribute values over time. This is crucial for accurate historical reporting. Common SCD types include Type 1 (overwrite), Type 2 (add new row), and Type 3 (add new column).
• Degenerate Dimensions: Dimensions that are derived directly from fact table columns, often used for transactional identifiers.
• Role-Playing Dimensions: A single physical dimension that can be referenced multiple times in a cube, each with a different role (e.g., a 'Date' dimension used as 'Order Date', 'Ship Date', and 'Delivery Date').

Dimension Properties

Each dimension attribute has various properties that affect its behavior and how it's used in the cube. Some important properties include:

Best Practices

• Design dimensions to reflect business processes and user analysis needs.
• Create meaningful hierarchies with appropriate levels.
• Use dimension attribute types correctly to optimize performance and functionality.
• Handle slowly changing dimensions appropriately for historical accuracy.
• Avoid overly complex or deep hierarchies that can hinder usability.
• Ensure primary keys are unique and stable.