Modeling Overview for Multidimensional Models

This section provides a foundational understanding of how to model data using SQL Server Analysis Services (SSAS) multidimensional models. Multidimensional models offer a powerful way to organize and analyze business data, enabling users to explore complex datasets through intuitive interfaces.

Key Concepts in Multidimensional Modeling

Multidimensional modeling revolves around two primary components:

1. Dimensions

Dimensions represent the business perspectives or attributes through which you analyze data. Common examples include:

• Time: Year, Quarter, Month, Day
• Geography: Country, State, City
• Product: Category, Subcategory, Product Name
• Customer: Segment, Individual Customer

Each dimension consists of hierarchies, which allow users to drill down or roll up through different levels of detail. For instance, a 'Time' dimension might have a hierarchy like Year > Quarter > Month.

2. Measures

Measures represent the numerical data that you want to analyze. These are typically quantitative values that can be aggregated. Examples include:

• Sales Amount
• Quantity Sold
• Profit
• Budget

Measures are usually stored in a fact table and can be aggregated using various functions such as SUM, COUNT, AVERAGE, MIN, and MAX.

Cubes: The Central Hub

Cubes are the fundamental data structures in a multidimensional model. They are built by combining dimensions and measures. A cube provides a multidimensional view of your business data, allowing users to query and analyze it from various angles.

Think of a cube as a data warehouse optimized for analysis. The intersection of dimension members (e.g., "2023", "North America", "Laptops") forms a cell within the cube, and the value in that cell is the measure (e.g., "Total Sales").

Process of Multidimensional Modeling

The typical process for creating a multidimensional model involves the following steps:

1. Define Business Requirements: Understand the analytical needs of the business users. What questions do they need to answer?
2. Identify Data Sources: Locate the source data, usually in relational databases (OLTP systems or data warehouses).
3. Design Dimensions: Create the dimension tables and define their attributes and hierarchies.
4. Design Fact Tables: Identify or create fact tables that contain the numerical measures and foreign keys to dimension tables.
5. Create the Cube: Use SSAS tools (like SQL Server Data Tools) to define the cube by linking dimensions to measures.
6. Process the Model: Load data from the source into the SSAS cube.
7. Deploy and Test: Deploy the cube to the SSAS server and test its performance and accuracy.

Advanced Modeling Concepts

• Aggregations: Pre-calculated summaries of data to improve query performance.
• Kpis (Key Performance Indicators): Define and track key business metrics.
• Calculated Members: Create custom measures or members that are not directly stored in the fact table.
• Perspectives: Define subsets of a cube tailored to specific user groups or business functions.

This overview provides a starting point for understanding multidimensional modeling in SSAS. The following sections will delve deeper into creating specific model elements like dimensions, measures, and cubes.