SQL Server Analysis Services: Multidimensional Modeling

Understanding Multidimensional Modeling in SSAS

Multidimensional modeling, also known as OLAP (Online Analytical Processing) modeling, is a foundational aspect of SQL Server Analysis Services (SSAS). It allows you to design and build cubes that provide fast, interactive analysis of business data. Unlike relational models optimized for transactional processing, multidimensional models are optimized for querying and analysis by business users.

The core components of a multidimensional model are Dimensions and Measures, which are organized into Cubes.

Dimensions: The "Who, What, Where, When, Why"

Dimensions represent the context for your data. They provide descriptive attributes that allow users to slice and dice measures. Common examples include Time, Geography, Product, and Customer. Dimensions are typically hierarchical, allowing users to analyze data at different levels of granularity (e.g., Year -> Quarter -> Month).

Measures: The "How Much"

Measures are the numerical values that you want to analyze, such as Sales Amount, Quantity Sold, or Profit. Measures are aggregated from underlying fact tables. SSAS supports various aggregation functions like Sum, Count, Average, Min, and Max.

Cubes: The Analytical Engine

A cube is a multidimensional data structure that combines measures and dimensions. It's the primary object that users interact with through reporting tools like Power BI, Excel, or custom applications. Cubes enable complex queries and provide high-performance data retrieval.

Key Concepts and Terminology

Designing a Multidimensional Model

The process involves several steps:

  1. Identify Business Requirements: Understand what questions users need to answer.
  2. Define Measures: Determine the key performance indicators (KPIs) to be tracked.
  3. Identify Dimensions: Determine the contextual attributes for analysis.
  4. Design Schemas: Typically, a star schema or snowflake schema is used in the relational source data.
  5. Create Cube: In SSAS, define the cube by selecting measures groups and dimensions.
  6. Build and Deploy: Process the cube to load data and then deploy it to the SSAS server.

Example: Simple Sales Cube Structure

Imagine a data warehouse with a 'Sales' fact table and 'Product', 'Date', and 'Store' dimension tables.

                
-- Fact Table Example (Sales)
CREATE TABLE FactSales (
    SalesKey INT PRIMARY KEY,
    ProductKey INT,
    DateKey INT,
    StoreKey INT,
    SalesAmount DECIMAL(18, 2),
    Quantity INT
);

-- Dimension Table Example (DimProduct)
CREATE TABLE DimProduct (
    ProductKey INT PRIMARY KEY,
    ProductName VARCHAR(100),
    Category VARCHAR(50),
    Subcategory VARCHAR(50)
);

-- Dimension Table Example (DimDate)
CREATE TABLE DimDate (
    DateKey INT PRIMARY KEY,
    FullDate DATE,
    Year INT,
    Month INT,
    MonthName VARCHAR(20),
    Quarter INT
);

-- Dimension Table Example (DimStore)
CREATE TABLE DimStore (
    StoreKey INT PRIMARY KEY,
    StoreName VARCHAR(100),
    City VARCHAR(50),
    Country VARCHAR(50)
);

In SSAS, you would create a cube that uses SalesAmount and Quantity as measures, and DimProduct, DimDate, and DimStore as dimensions. Hierarchies would be built, for example, from Year to Month in the DimDate dimension.

Advantages of Multidimensional Modeling