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Build an OLAP Cube in SSAS from Google Cloud Storage Data

Connect to Google Cloud Storage data in an Analysis Services project using the Google Cloud Storage Data Provider and build OLAP cubes for use in analytics, reporting, and more.

SQL Server Analysis Services (SSAS) is an analytical data engine used in decision support and business analytics. It provides enterprise-grade semantic data models for business reports and client applications, such as Power BI, Excel, Reporting Services reports, and other data visualization tools. When paired with the CData ADO.NET Provider for Google Cloud Storage, you can create cubes from Google Cloud Storage data for deeper and faster data analysis.

This article walks through creating an Analysis Services project in Visual Studio to build and deploy a multi-dimensional model of Google Cloud Storage data. You will need to have an accessible SSAS instance and the ADO.NET Provider installed.

Creating a Data Source for Google Cloud Storage

Start by creating a new Analysis Service Multidimensional and Data Mining Project in Visual Studio. Next, create a Data Source for Google Cloud 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 Google Cloud Storage, enter the necessary connection properties, and click Next.

    Authenticate with a User Account

    You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

    When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

    Authenticate with a Service Account

    Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

    You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

    • InitiateOAuth: Set this to GETANDREFRESH.
    • OAuthJWTCertType: Set this to "PFXFILE".
    • OAuthJWTCert: Set this to the path to the .p12 file you generated.
    • OAuthJWTCertPassword: Set this to the password of the .p12 file.
    • OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
    • OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
    • OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
    • ProjectId: Set this to the Id of the project you want to connect to.

    The OAuth flow for a service account then completes.

    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 Google Cloud 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 Google Cloud Storage Source) and click Next.
  3. Choose a foreign key match pattern that matches your underlying data source and click Next.
  4. Select Google Cloud 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 Google Cloud Storage

The last step before you can process the project and deploy Google Cloud 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 Google Cloud 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 Google Cloud Storage.