Import Azure Table Data Using Azure Data Factory

Microsoft Azure Data Factory (ADF) is a completely managed, serverless data integration service. When combined with CData Connect AI, ADF enables immediate cloud-to-cloud access to Azure Table data within data flows. This article outlines the process of connecting to Azure Table through Connect AI and accessing Azure Table data within ADF.

CData Connect AI offers a cloud-to-cloud interface tailored for Azure Table, granting you the ability to access live data from Azure Table data within Azure Data Factory without the need for data replication to a natively supported database. Equipped with optimized data processing capabilities by default, CData Connect AI seamlessly channels all supported SQL operations, including filters and JOINs, directly to Azure Table. This harnesses server-side processing to expedite the retrieval of the desired Azure Table data.

Configure Azure Table Connectivity for ADF

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

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

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

2. Select "Azure Table" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Azure Table.

   Specify your AccessKey and your Account to connect. Set the Account property to the Storage Account Name and set AccessKey to one of the Access Keys. Either the Primary or Secondary Access Keys can be used. To obtain these values, navigate to the Storage Accounts blade in the Azure portal. You can obtain the access key by selecting your account and clicking Access Keys in the Settings section.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Azure Table Connection page and update the User-based permissions. 👁 Updating 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. 👁 Creating a new PAT
   
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 Azure Table data from Azure Data Factory.

Access Live Azure Table Data in Azure Data Factory

To establish a connection from Azure Data Factory to the CData Connect AI Virtual SQL Server API, follow these steps.

1. Login to Azure Data Factory.
👁 Logging in to ADF

2. If you have not yet created a Data Factory, Click New -> Dataset.
👁 Creating new data factory

3. In the search bar, enter SQL Server and select it when it appears. On the following screen, enter a name for the server. In the Linked service field, select New.
👁 Selecting SQL Server

4. Enter the connection settings.
   • Name - enter a name of your choice.
   • Server name - enter the Virtual SQL Server endpoint and port separated by a comma: tds.cdata.com,14333
   • Database name - enter the Connection Name of the CData Connect AI data source you want to connect to (for example, AzureTables1).
   • 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 - select Password (not Azure Key Vault) and enter the PAT you generated on the Settings page.
   • Click Create.
👁 Configuring new linked service

5. In Set properties, set the Name, choose the Linked service we just created, select a Table name from those available, and Import schema from connection/store. Click OK.
👁 Setting the properties

6. After creating the linked service, the following screen should appear:
👁 Displaying the new screen

7. Click preview data to see the imported Azure Table table.
👁 Previewing the imported table
You can now use this dataset when creating data flows in Azure Data Factory.

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