Creating a Multidimensional Model (SSDT)

This guide walks you through the process of creating a multidimensional model using SQL Server Data Tools (SSDT) in Visual Studio. Multidimensional models provide a powerful way to analyze data by organizing it into cubes, dimensions, and measures.

Prerequisites

• SQL Server installed with Analysis Services.
• Visual Studio installed.
• SQL Server Data Tools (SSDT) installed for your version of Visual Studio.
• Access to a data source (e.g., SQL Server database).

Steps to Create a Multidimensional Model

1. Create a New Analysis Services Project

1. Open Visual Studio.
2. Go to File > New > Project....
3. In the New Project dialog, expand Business Intelligence and select Analysis Services.
4. Choose the Analysis Services Multidimensional Project template.
5. Enter a Name for your project (e.g., `SalesAnalysisModel`) and a Location.
6. Click OK.

2. Create a Data Source

A data source defines the connection to your underlying data. Typically, this will be your SQL Server database.

1. In Solution Explorer, right-click on the Data Sources folder and select Add New Data Source....
2. The Data Source Wizard will open. Click Next.
3. Choose Microsoft SQL Server as the data source type and click Next.
4. Click New... to configure the connection.
5. Enter the Server name and select the Database name.
6. Click OK, then click Next.
7. Review the connection string and click Finish.

3. Create a Data Source View

A Data Source View (DSV) is a logical representation of the data from one or more data sources. It allows you to define relationships, calculated columns, and hide unnecessary data.

1. In Solution Explorer, right-click on the Data Source Views folder and select Add New Data Source View....
2. The Data Source View Wizard will open. Click Next.
3. Select the data source you created in the previous step and click Next.
4. From the list of tables and views, select the tables you need for your model (e.g., `DimProduct`, `DimCustomer`, `FactSales`).
5. SSDT will try to automatically detect relationships. You can review and modify them here. Click Next.
6. Review the selected items and click Finish.

You can now open the DSV designer to further refine your data source view.

4. Create Cubes

Cubes are the core of a multidimensional model. They are built from dimensions and measures, providing a multidimensional structure for data analysis.

1. In Solution Explorer, right-click on the Cubes folder and select New Cube....
2. The Cube Wizard will open. Click Next.
3. Choose to use an existing data source view and select the DSV you created. Click Next.
4. Specify how you want to create the cube:
   • Use existing tables: SSDT can automatically detect fact and dimension tables.
   • Create a new cube manually: You define dimensions and measures explicitly.
   For this example, let's assume SSDT can detect your tables. Click Next.
5. If SSDT detected fact tables, select the primary fact table (e.g., `FactSales`). Then, select the associated measure groups and measures (e.g., `SalesAmount`, `Quantity`). Click Next.
6. Select the dimensions you want to include in the cube. SSDT will often suggest dimensions based on foreign key relationships. Click Next.
7. Review the summary and click Finish.

The Cube Designer will open, allowing you to configure dimensions, measures, calculations, and other cube properties.

5. Deploy the Model

After designing your model, you need to deploy it to an Analysis Services instance.

1. Right-click on the project in Solution Explorer and select Properties.
2. In the Project Properties dialog, set the Server property to the name of your Analysis Services instance.
3. Click OK.
4. Right-click on the project again and select Deploy.

This process compiles the model and deploys it to the specified Analysis Services server. You can then connect to this deployed model using client tools like Excel, Power BI, or SQL Server Management Studio.

Best Practices

• Use clear and descriptive names for all objects (cubes, dimensions, measures).
• Design your dimensions carefully, including hierarchies where appropriate.
• Define appropriate aggregation methods for your measures.
• Leverage calculated members and KPIs to provide richer analytical insights.
• Test your model thoroughly after deployment.