Azure Data Factory: Schedule, Trigger, and Monitor

This integration allows users to schedule, trigger, and monitor the Azure Data Factory pipeline process directly from the Universal Controller. 

Key Features:

• Uses Python modules azure-mgmt-resource and azure-mgmt-datafactory to make REST API calls to Azure Data Factory.
• Use the Azure tenant ID, subscription ID, client ID, client secret, resource group, and location for authenticating the REST API calls to Azure Data Factory. 
• Perform the following Azure Data Factory operations:
  -Run a pipeline.
  -Get a pipeline info.
  -List all pipelines.
  -Cancel pipeline run.
  -List factory by resource group.
• Azure Data Factory triggers user can perform the following operations from UAC:
  -Start trigger.
  -Stop trigger.
  -List trigger by factory.
• UAC also can restart a failed pipeline either from the failed step or from any activity name in the failed pipeline. 

What's New

Enhancements

• Added: New "Authentication Method" field to select between Service Principal, Workload Identity and Managed Identity. Defaults to Service Principal.
• Added: Managed Identity authentication, using a managed identity of the Azure resource hosting the Agent. An empty credential selects the system-assigned identity; a credential whose Runtime User is a Client ID selects that user-assigned identity.
• Added: Workload Identity authentication, using the federated token file referenced by the AZURE_FEDERATED_TOKEN_FILE environment variable on the Agent.
• Changed: "Authenticate ADF via Certificates" now acts as an override of the selected Authentication Method.
• Changed: "Tenant ID" is hidden when Managed Identity is selected, as it is not required. "Azure Data Factory Credentials" remains visible and optional, so a user-assigned identity can be specified.