Import Vault CRM Data Using Azure Data Factory

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

CData Connect Cloud offers a cloud-to-cloud interface tailored for Vault CRM, granting you the ability to access live data from Vault CRM 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 Cloud seamlessly channels all supported SQL operations, including filters and JOINs, directly to Vault CRM. This harnesses server-side processing to expedite the retrieval of the desired Vault CRM data.

Configure Vault CRM Connectivity for ADF

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

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

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

2. Select "Vault CRM" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Vault CRM.

   You are ready to connect after specifying the following connection properties:

   • Url: The host you see in the URL after you login to your account. For example: https://my-veeva-domain.veevavault.com
   • User: The username you use to login to your account.
   • Password: The password you use to login to your account.
   
4. Click Create & Test
5. Navigate to the Permissions tab in the Add Vault CRM Connection page and update the User-based permissions.

Add a Personal Access Token

When connecting to Connect Cloud 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 Cloud. 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 Cloud 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 Vault CRM data from Azure Data Factory.

Access Live Vault CRM Data in Azure Data Factory

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

1. Login to Azure Data Factory.

2. If you have not yet created a Data Factory, Click New -> Dataset.

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.

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 Cloud data source you want to connect to (for example, VaultCRM1).
   • 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 - select Password (not Azure Key Vault) and enter the PAT you generated on the Settings page.
   • Click Create.

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.

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

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

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