Build an OLAP Cube in SSAS from Azure Data Lake Storage Data

SQL Server Analysis Services (SSAS) serves as an analytical data engine employed in decision support and business analytics, offering high-level semantic data models for business reports and client applications like Power BI, Excel, Reporting Services reports, and various data visualization tools. When coupled with the CData ADO.NET Provider for Azure Data Lake Storage, you gain the capability to generate cubes from Azure Data Lake Storage data, facilitating more profound and efficient data analysis.

In this article, we will guide you through the process of developing and deploying a multi-dimensional model of Azure Data Lake Storage data by creating an Analysis Services project in Visual Studio. To proceed, ensure that you have an accessible SSAS instance and have installed the ADO.NET Provider.

Creating a Data Source for Azure Data Lake Storage

Start by creating a new Analysis Service Multidimensional and Data Mining Project in Visual Studio. Next, create a Data Source for Azure Data Lake Storage data in the project.

1. In the Solution Explorer, right-click Data Source and select New Data Source.
2. Opt to create a data source based on an existing or new connection and click New.
3. In the Connection Manager, select CData ADO.NET Provider for Azure Data Lake Storage, enter the necessary connection properties, and click Next.

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Azure AD for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the .
   2. Select "Azure Active Directory".
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   When you configure the connection, you may also want to set the Max Rows connection property. This will limit the number of rows returned, which is especially helpful for improving performance when designing reports and visualizations.

4. Set the impersonation method to Inherit and click Next.
5. Name the data source (CData Azure Data Lake Storage Source) and click Finish.

Creating a Data Source View

After you create the data source, create the data source view.

1. In the Solution Explorer, right-click Data Source Views and select New Data Source View.
2. Select the data source you just created (CData Azure Data Lake Storage Source) and click Next.
3. Choose a foreign key match pattern that matches your underlying data source and click Next.
4. Select Azure Data Lake Storage tables to add to the view and click Next.
5. Name the view and click Finish

Based on the foreign key match scheme, relationships in the underlying data will be automatically detected. You can view (and edit) these relationships by double clicking Data Source View.

Note that adding a secondary data source to the Data Source View is not supported. When working with multiple data sources, SSAS requires both sources to support remote queries via OpenRowset which is unavailable in the ADO.NET Provider.

Creating a Cube for Azure Data Lake Storage

The last step before you can process the project and deploy Azure Data Lake Storage data to SSAS is creating the cubes.

1. In the Solution Explorer, right-click Cubes and select New Cube
2. Select "Use existing tables" and click Next.
3. Select the tables that will be used for measure group tables and click Next.
4. Select the measures you want to include in the cube and click Next.
5. Select the dimensions to be created, based on the available tables, and click Next.
6. Review all of your selections and click Finish.

Process the Project

With the data source, data source view, and cube created, you are ready to deploy the cube to SSAS. To configure the target server and database, right-click the project and select properties. Navigate to deployment and configure the Server and Database properties in the Target section.

After configuring the target server and database, right-click the project and select Process. You may need to build and deploy the project as a part of this step. Once the project is built and deployed, click Run in the Process Database wizard.

Now you have an OLAP cube for Azure Data Lake Storage data in your SSAS instance, ready to be analyzed, reported, and viewed. Get started with a free, 30-day trial of the CData ADO.NET Provider for Azure Data Lake Storage.