MSDN Documentation

Designing Multidimensional Models

This section guides you through the process of designing effective multidimensional models for SQL Server Analysis Services. Learn best practices for structuring cubes, dimensions, and measures to optimize performance and usability for business intelligence solutions.

Understanding the Core Components

A multidimensional model in Analysis Services is built upon several key components that work together to provide a rich analytical experience. Understanding these components is the first step towards effective design.

• Cubes: The central analytical object, containing measures and dimensions.
• Dimensions: Hierarchical structures that allow users to slice and dice data (e.g., Time, Geography, Product).
• Measures: Numerical values that users can aggregate and analyze (e.g., Sales Amount, Quantity Sold).
• Schemas: The logical and physical structure of the data source views used to build the cube.

Best Practices for Dimension Design

Well-designed dimensions are crucial for usability and performance. Consider the following:

• Attribute Granularity: Determine the lowest level of detail for each attribute.
• Hierarchies: Define meaningful hierarchies that reflect business structure (e.g., Year > Quarter > Month > Day).
• Natural Hierarchies: Utilize natural hierarchies where applicable, such as in time dimensions.
• Parent-Child Hierarchies: Use for data with an unknown or variable depth (e.g., organizational charts).
• Member Names and Keys: Ensure consistent and unique member names and keys.

Designing Effective Measure Groups

Measure groups group related measures and define their relationship with dimensions.

• Fact Tables: Identify the fact tables that contain the transactional data for your measures.
• Aggregation Design: Plan aggregations to improve query performance. Pre-calculating common aggregations can significantly reduce query response times.
• Measure Types: Understand and use appropriate measure types (e.g., Sum, Count, Average, Distinct Count).
• Semi-Additive Measures: Implement semi-additive measures for financial data where measures can be aggregated across some dimensions but not others.

Structuring Your Cube

The overall structure of your cube impacts how users interact with the data.

• Star Schema vs. Snowflake Schema: While Analysis Services can work with both, a star schema is often preferred for its simplicity and performance.
• Dimension Usage: Define how dimensions relate to measure groups (e.g., Facted, Referenced, Many-to-Many).
• Perspectives: Create different views of the cube to simplify access for specific user groups.
• Roles: Implement security by defining roles with specific permissions on cubes, dimensions, and measures.

Advanced Design Considerations

Beyond the basics, several advanced techniques can further enhance your multidimensional models:

• Calculated Members and Measures: Define complex calculations directly within the cube.
• Actions: Create actions to link from cube data to external resources or other reports.
• Translations: Support multiple languages for dimension members and measure names.
• Writeback: Enable users to write data back into the cube for planning scenarios.

Example: Designing a Sales Cube

Consider a simple sales scenario. You might have a fact table with sales transactions (quantity, amount) and dimension tables for Products, Customers, and Dates.

-- Example Fact Table: FactSales
CREATE TABLE FactSales (
    ProductID INT,
    CustomerID INT,
    DateKey INT,
    Quantity INT,
    SalesAmount DECIMAL(10, 2)
);

-- Example Dimension Table: DimProduct
CREATE TABLE DimProduct (
    ProductID INT PRIMARY KEY,
    ProductName VARCHAR(255),
    Category VARCHAR(100),
    Subcategory VARCHAR(100)
);

-- Example Dimension Table: DimDate
CREATE TABLE DimDate (
    DateKey INT PRIMARY KEY,
    FullDate DATE,
    Year INT,
    Quarter INT,
    Month INT,
    Day INT
);
            

In Analysis Services, you would create a cube with measures like SUM(SalesAmount) and SUM(Quantity), and dimensions for Product, Customer, and Date, potentially including hierarchies for Date (Year/Quarter/Month) and Product (Category/Subcategory/Product Name).