Model REST 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 Cloud, AAS facilitates immediate cloud-to-cloud access to REST data for applications. This article outlines the process of connecting to REST via Connect Cloud and importing REST data into Visual Studio using an AAS extension.

CData Connect Cloud offers a seamless cloud-to-cloud interface tailored for REST, enabling you to create live models of REST 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 Cloud is equipped with optimized data processing capabilities right from the start, directing all supported SQL operations, including filters and JOINs, directly to REST. This leverages server-side processing for swift retrieval of the requested REST data.

Prerequisites

Before you connect, you must first do the following:

• Connect a data source to your CData Connect Cloud 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 Cloud.
• Install and configure an On-Premise Gateway in your system. This will pull data from the source via CData Connect Cloud into the Azure Analysis Services project and deploy models to the server. Refer to the given link to find the detailed process.

Configure REST Connectivity for AAS

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

1. Log into Connect Cloud, click Connections and click Add Connection

2. Select "REST" from the Add Connection panel

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

   See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models REST APIs as bidirectional database tables and XML/JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

   After setting the URI and providing any authentication values, set Format to "XML" or "JSON" and set DataModel to more closely match the data representation to the structure of your data.

   The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

   • Document (default): Model a top-level, document view of your REST data. The data provider returns nested elements as aggregates of data.
   • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
   • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

   See the Modeling REST Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

   
4. Click Create & Test
5. Navigate to the Permissions tab in the Add REST Connection page and update the User-based permissions.

Add a Personal Access Token

If you are connecting from a service, application, platform, or framework that lacks support for OAuth authentication, you have the option to generate a Personal Access Token (PAT) for authentication purposes. It's advisable to follow best practices by creating a distinct PAT for each service to uphold access granularity.

1. Click on your username at the top right of the Connect Cloud app and click User Profile.
2. On the User Profile page, scroll down to the Personal Access Tokens section and click Create PAT.
3. Give your 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, you are ready to connect to REST data from Visual Studio using Azure Analysis Services.

Connect to REST in Visual Studio Using AAS

The steps below outline connecting to CData Connect Cloud from Azure Analysis Services to create a new REST 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 Cloud username. This is displayed in the top-right corner of the CData Connect Cloud 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 REST 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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