Developing SSAS Multidimensional Models: A Comprehensive Tutorial

1. Introduction

This tutorial provides a step-by-step guide to developing a multidimensional model using SQL Server Analysis Services (SSAS). Multidimensional models are a cornerstone of business intelligence, enabling users to analyze vast amounts of data from various perspectives. We will cover the entire development lifecycle, from project setup to model exploration.

2. Prerequisites

Before you begin, ensure you have the following installed:

• SQL Server 2019 or later (with Analysis Services instance)
• SQL Server Data Tools (SSDT) for Visual Studio
• A relational database containing sample data (e.g., AdventureWorksDW)

3. Setting Up SQL Server Data Tools (SSDT)

SQL Server Data Tools is essential for SSAS development. If you don't have it, download and install it from the official Microsoft website. Ensure you select the "Business Intelligence" workload during installation.

You can download SSDT from: Microsoft Learn: Download SSDT

4. Creating a New SSAS Project

1. Open Visual Studio.
2. Go to File > New > Project...
3. In the "New Project" dialog, navigate to Business Intelligence > Analysis Services.
4. Select Analysis Services Multidimensional and Tabular project.
5. Name your project (e.g., "SalesAnalysisModel") and choose a location. Click OK.

A new project will be created, and the Solution Explorer will open, showing the main project components like Data Sources, Data Source Views, Dimensions, Cubes, and Roles.

5. Defining Data Sources

The data source represents the connection to your relational database.

1. In Solution Explorer, right-click on Data Sources and select New Data Source...
2. Click New... to create a new connection.
3. In the "TableAdapter Manager" or "Data Source Wizard," specify your server name and database name.
4. Test the connection and click OK to finish.

6. Configuring Data Source Views

A Data Source View (DSV) is a logical representation of your relational data. It allows you to define relationships, rename objects, and filter data before it's used in your multidimensional model.

1. In Solution Explorer, right-click on Data Source Views and select New Data Source View...
2. Select the data source you created in the previous step.
3. From the available tables and views, select those relevant to your analysis (e.g., FactInternetSales, DimProduct, DimCustomer, DimDate).
4. Click Finish.

You can now add named queries, define relationships between tables, and create calculated columns within the DSV designer.

7. Building Dimensions

Dimensions represent the attributes by which you want to analyze your data (e.g., Time, Product, Customer).

7.1. Time Dimension

A time dimension is crucial for time-based analysis.

1. In Solution Explorer, right-click on Dimensions and select New Dimension...
2. Choose Create dimension from existing table and click Next.
3. Select the DimDate table from your DSV.
4. Choose the columns to include (e.g., FullDateAlternateKey as the key, and columns like Year, Month, Day, etc.).
5. Configure the dimension type (e.g., Time) and finish the wizard.

7.2. Product Dimension

Similar steps apply to creating a product dimension.

1. Create a new dimension.
2. Select the DimProduct table.
3. Choose a primary key (e.g., ProductKey) and descriptive attributes (e.g., EnglishProductName, ProductCategoryName).

7.3. Customer Dimension

Create a dimension for customer analysis.

1. Create a new dimension.
2. Select the DimCustomer table.
3. Choose a primary key (e.g., CustomerKey) and relevant attributes (e.g., FirstName, LastName, EmailAddress, GeographyKey).

You can further enhance dimensions by creating hierarchies (e.g., Year > Quarter > Month for the Time dimension).

8. Designing Cubes

Cubes are the core of a multidimensional model, holding measures and dimensions for analysis.

8.1. Measure Groups

Measure groups contain measures and are linked to fact tables.

1. In Solution Explorer, right-click on Cubes and select New Cube...
2. Choose Create cube from existing table.
3. Select the FactInternetSales table as your main fact table.
4. The wizard will suggest measures and dimensions based on your DSV.

8.2. Measures

Measures are the numerical values you want to aggregate (e.g., SalesAmount, Quantity).

Within the Cube designer:

• Drag and drop measures from the data source pane (e.g., SalesAmount, OrderQuantity).
• Configure aggregation types (e.g., Sum, Count, Average).
• Drag and drop your created dimensions (Time, Product, Customer) into the cube designer to associate them with the measures.

9. Deploying and Processing the Model

Once your model is designed, you need to deploy it to the SSAS server and process it to load the data.

1. Right-click on your SSAS project in Solution Explorer and select Properties.
2. Under Configuration Properties > Deployment, ensure the Server name property is set to your SSAS instance.
3. Right-click on the project again and select Deploy.
4. After successful deployment, right-click on the project and select Process.

Processing updates the cube with the latest data from your relational sources.

10. Exploring the Model

After processing, you can explore your SSAS model using tools like SQL Server Management Studio (SSMS) or by connecting from a BI client tool like Power BI or Excel.

In SSMS:

1. Connect to your SSAS instance.
2. Navigate to Databases > [YourDatabaseName] > Cubes > [YourCubeName].
3. Right-click on the cube and select Browse.

You can then drag and drop measures and dimensions to create ad-hoc queries and visualizations.

Example MDX query:

11. Conclusion

Developing SSAS multidimensional models is a powerful way to create robust business intelligence solutions. By following these steps, you have learned how to set up your project, define data sources, build dimensions and cubes, deploy, and explore your model. Continuous refinement and addition of advanced features like calculations, KPIs, and security roles will further enhance your models.