Model GitLab Data Using Azure Analysis Services

Microsoft Azure Analysis Services (AAS) is a fully-managed platform-as-a-service (PaaS) offering that delivers enterprise-grade data models in the cloud. When combined with CData Connect AI, AAS facilitates immediate cloud-to-cloud access to GitLab data for applications. This article outlines the process of connecting to GitLab via Connect AI and importing GitLab data into Visual Studio using an AAS extension.

CData Connect AI offers a seamless cloud-to-cloud interface tailored for GitLab, enabling you to create live models of GitLab data in Azure Analysis Services without the need to replicate data to a natively supported database. While constructing high-quality semantic data models for business reports and client applications, Azure Analysis Services formulates SQL queries to retrieve data. CData Connect AI is equipped with optimized data processing capabilities right from the start, directing all supported SQL operations, including filters and JOINs, directly to GitLab. This leverages server-side processing for swift retrieval of the requested GitLab data.

Prerequisites

Before you connect, you must first do the following:

• Connect a data source to your CData Connect AI account. Detailed steps are provided in the next section.
• Generate a Personal Access Token (PAT). Copy this down, as it acts as your password during authentication.
• Create a server in Azure Analysis Services to which you will deploy your data from CData Connect AI.
• Install and configure an On-Premise Gateway in your system. This will pull data from the source via CData Connect AI into the Azure Analysis Services project and deploy models to the server. Refer to the given link to find the detailed process.

Configure GitLab Connectivity for AAS

Connectivity to GitLab from Azure Analysis Services is made possible through CData Connect AI. To work with GitLab data from Azure Analysis Services, we start by creating and configuring a GitLab connection.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "GitLab" from the Add Connection panel

3. Enter the necessary authentication properties to connect to GitLab.

   To connect to GitLab, use either OAuth or a personal access token:

   Using OAuth

   Register an OAuth application in GitLab under Edit Profile > Applications (or group Settings > Applications). Set the Redirect URI to https://oauth.cdata.com/oauth/. Note the Application ID (OAuth Client Id) and Secret (shown once). Then set:

   • OAuth Client Id: The Application ID from your GitLab OAuth application.
   • OAuth Client Secret: The Secret from your GitLab OAuth application.

   Click Sign In to complete OAuth authentication.

   Using a Personal Access Token

   In GitLab, navigate to Edit Profile > Access Tokens > Add new token. Select the required scopes (such as api, read_api, read_user, read_repository) and set an expiration date. Copy the token immediately (shown only once). Then set:

   • API Key: The personal access token from your GitLab account.
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add GitLab Connection page and update the User-based permissions.

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to GitLab data from Visual Studio using Azure Analysis Services.

Connect to GitLab in Visual Studio Using AAS

The steps below outline connecting to CData Connect AI from Azure Analysis Services to create a new GitLab data source. You will need the Microsoft Analysis Services Project extension installed in Microsoft Visual Studio to continue.

1. In Visual Studio, create a new project. Select Analysis Services Tabular Project. Click on Next.

2. In the Configure your new project dialog box, enter a name for your project in the Project name field. Fill in the rest of the fields.

3. Click on Create. The Tabular model designer dialog box opens. Select Workspace server and enter the address of your Azure Analysis Services server (for example, asazure://eastus.azure.windows.net/myAzureServer). Also, make sure to select the option SQL Server 2022 / Azure Analysis Services (1600) from the Compatibility level dropdown. Click on Test Connection to check if the connection details are correct. Click OK and sign in to your server.

4. Now, click on OK to create the project. Your Visual Studio window should resemble the following screenshot:

5. In the Tabular Model Explorer window of Visual Studio, right-click Data Sources and select Import From Data Source.

6. In the Get Data window, select SQL Server database and click Connect. In the Server field, enter the Virtual SQL Server endpoint and the port separated by a comma: e.g., “tds.cdata.com, 14333”, and click on OK.

7. Click on Database and enter the following information:
   • User name: Enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected].
   • Password: Enter the PAT you generated on the Settings page.

   Click on Connect. If successful, the Navigator window will pop up.

   
8. In the Navigator window, search and select the tables of your choice
9. You should now see the Salesforce table populated with data in the preview section on the right panel.
10. Click on Load to import the data.

Now that you have imported the GitLab data into your data model, you are ready to deploy the project to Azure Analysis Services for use in business reports, client applications, and more.

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