Getting Started with Multidimensional Modeling in Analysis Services

Welcome to the essential guide for multidimensional modeling in SQL Server Analysis Services (SSAS). This section will walk you through the fundamental concepts, architecture, and initial steps required to build powerful analytical solutions.

What is Multidimensional Modeling?

Multidimensional modeling is a technique used in business intelligence to represent data in a way that is optimized for analysis and querying. Instead of traditional relational tables, data is organized into cubes, which are composed of:

• Dimensions: These represent the business perspectives or categories through which you want to analyze data (e.g., Time, Geography, Products, Customers).
• Measures: These are the quantitative values that you want to aggregate and analyze (e.g., Sales Amount, Quantity Sold, Profit).

This structure allows for fast and intuitive data exploration, enabling users to slice, dice, and drill down into data with ease.

Key Concepts

Cubes

A cube is the core object in SSAS multidimensional models. It's a data structure that aggregates data from fact tables and organizes it around dimensions. Think of it as a multidimensional spreadsheet where you can view data from various angles.

Dimensions

Dimensions provide the context for your measures. They are typically derived from lookup tables in your data warehouse. Each dimension contains hierarchies, which define the levels of aggregation (e.g., a 'Time' dimension might have Year > Quarter > Month > Day).

Measures

Measures are the numerical data points that you want to analyze. They are usually derived from columns in fact tables. Common aggregation functions for measures include SUM, COUNT, AVERAGE, MIN, and MAX.

Hierarchies

Hierarchies are ordered sets of attributes within a dimension that represent different levels of granularity. Users navigate these hierarchies to drill down or roll up data.

Kpis (Key Performance Indicators)

KPIs are calculations that measure performance against business objectives. They provide a quick way to assess success and identify areas needing attention.

Steps to Get Started

1. Understand Your Data Source

   Before modeling, it's crucial to understand the underlying relational data sources (e.g., SQL Server databases, data warehouses). Identify your fact tables (containing measures) and dimension tables (containing descriptive attributes).

2. Design Your Model

   Plan the structure of your cube. Decide which dimensions will be associated with your fact tables and define the measures you need. Consider how users will interact with the data.

3. Create a New Analysis Services Project

   In Visual Studio with SQL Server Data Tools (SSDT) installed, create a new "Analysis Services Project."

   
   File -> New -> Project -> Business Intelligence -> Analysis Services Project
                       

4. Define Data Sources and Data Source Views

   Connect to your data sources and create Data Source Views (DSVs). A DSV is a logical representation of your relational data, allowing you to shape, clean, and define relationships without altering the original source.

5. Create Dimensions

   Create dimensions based on your dimension tables. Configure hierarchies and attribute relationships within each dimension.

6. Create a Cube

   Build your cube by selecting the fact table and the dimensions you want to include. Define your measures and configure measure groups.

7. Deploy and Process

   Deploy your SSAS project to an Analysis Services instance and then process the cube. Processing populates the cube with data from your data sources.

8. Query Your Cube

   Use tools like SQL Server Management Studio (SSMS), Excel, or other BI clients to connect to and query your cube using MDX (Multidimensional Expressions) or DAX (Data Analysis Expressions).

This introduction provides a high-level overview. The subsequent sections will delve deeper into each aspect of multidimensional modeling, from detailed dimension design to cube creation and querying.