Dimension Design in Analysis Services Multidimensional Models

This document provides a comprehensive guide to designing dimensions for multidimensional models in SQL Server Analysis Services (SSAS). Dimensions are fundamental to business intelligence solutions, enabling users to slice and dice data for analysis.

Introduction to Dimensions

In a multidimensional model, dimensions provide the context for the measures in your cubes. For example, if you have a measure for 'Sales Amount', dimensions like 'Time', 'Product', and 'Geography' allow you to analyze sales by year, by product category, or by region. Well-designed dimensions are crucial for intuitive and effective data analysis.

Types of Dimensions

Analysis Services supports several types of dimensions, each suited for different scenarios:

• Standard Dimensions: The most common type, representing business entities like customers, products, or dates.
• Degenerate Dimensions: Attributes that do not have a natural dimension table of their own but are promoted from a fact table, such as invoice numbers or transaction IDs.
• Role-Playing Dimensions: A single dimension instance that can be used multiple times in a cube, each with a different role. A common example is a 'Date' dimension used for 'Order Date', 'Ship Date', and 'Delivery Date'.
• Parent-Child Dimensions: Dimensions that represent hierarchical data where the parent-child relationship is stored within a single table, like organizational structures or employee hierarchies.

Creating Dimensions

Dimensions can be created using SQL Server Data Tools (SSDT) for Visual Studio. The Dimension Wizard guides you through the process of selecting a data source, defining attributes, and establishing relationships. You can create dimensions based on existing tables or views in your relational data source.

Dimension Attributes

Attributes are the columns within a dimension that describe its members. For example, in a 'Product' dimension, attributes might include 'Product Name', 'Product Category', 'Brand', and 'Color'. Attributes are the building blocks for hierarchies and provide the granular level of detail for analysis.

Key attribute properties include:

• Name: The display name of the attribute.
• Key Column: The column in the dimension table that uniquely identifies each member.
• Name Column: The column that provides the human-readable name for the member.
• Attribute Type: (e.g., Regular, Key, Date).

Attribute Relationships

Attribute relationships define how attributes relate to each other within a dimension. These relationships are crucial for query performance and the proper functioning of hierarchies. A 'One-to-Many' relationship is the most common, where one member of a higher-level attribute corresponds to many members of a lower-level attribute (e.g., one 'Category' to many 'Products').

Types of attribute relationships:

• One-to-Many: The default and most common relationship.
• Many-to-Many: Used when a member of one attribute can be associated with multiple members of another attribute. Requires careful design.
• Fat Key: A single attribute that contains information typically spread across multiple attributes.

Hierarchies

Hierarchies allow users to navigate data at different levels of granularity. They are built using attribute relationships. For example, a 'Geography' hierarchy might be structured as 'Continent' -> 'Country' -> 'State' -> 'City'. Analysis Services allows you to create both user-defined hierarchies and natural (or natural) hierarchies.

Processing Dimensions

After creating or modifying a dimension, it needs to be processed. Processing updates the dimension's internal structures based on the data in the source tables. Incremental processing can be used to update only the changes, significantly reducing processing time for large dimensions.

Best Practices

• Keep Dimensions Lean: Avoid including too many attributes in a single dimension. Consider splitting complex entities into separate dimensions.
• Normalize Source Data: Ensure your dimension tables are normalized to avoid redundancy and data integrity issues.
• Use Meaningful Names: Name dimensions and attributes clearly to enhance usability.
• Design for Performance: Carefully define attribute relationships to optimize query performance.
• Implement Aggregations: While not directly part of dimension design, understanding how dimensions are used in fact tables helps in designing effective aggregations.
• Handle Slowly Changing Dimensions (SCDs): Understand and implement appropriate SCD types (Type 1, Type 2, etc.) to manage historical data effectively.

-- Sample SQL for creating a dimension table
CREATE TABLE DimProduct (
    ProductKey INT PRIMARY KEY,
    ProductName VARCHAR(100),
    ProductCategory VARCHAR(50),
    Brand VARCHAR(50)
);

-- In SSDT, you would select this table and designate ProductKey as the Key Column
-- and ProductName as the Name Column for the 'Product' dimension.
-- ProductCategory and Brand would be regular attributes.