JAMS Azure Data Factory integration
Azure Data Factory Pipeline Orchestration for Hybrid Workloads
JAMS Scheduler starts Azure Data Factory pipelines when your on-premises jobs finish, passes them parameters, and cancels child pipelines along with the parent.
Execution method
ADFPipelineRuns
Needs
JAMS Scheduler and Client 7.8.1 or later
Cancel cleanly
Canceling a run cancels its child pipelines too
What JAMS Scheduler Runs in Azure Data Factory
Name the resource group, data factory, and pipeline. Parameters you add on the job become pipeline run parameters. Schedules, dependencies, and alerts work like any other job.
How JAMS Scheduler Connects to Azure Data Factory
You’ll need four values from Azure: Tenant ID, Client ID, Client Secret, and Subscription ID. They go into an Azure connection in the JAMS Connection Store, with the client secret stored as a JAMS credential.
A second credential sets the account the job runs as. Jobs can run on the JAMS Scheduler or on an agent.
Requirements
JAMS version
7.8.1 or later, Scheduler and Client
Azure values
Tenant ID, Client ID, Client Secret, Subscription ID
Credentials
One for the client secret, one to run the job
Azure Data Factory in a Hybrid Workflow
A SQL Server job finishes on-premises, the ADF pipeline starts in Azure, and a Python or SSRS job picks up the result. All three show up in one job history.
For the wider cloud picture, see JAMS Scheduler on Azure.
Azure Data Factory FAQ
Can JAMS Scheduler start an ADF pipeline after an on-premises job?
Yes. An ADFPipelineRuns job takes the same dependencies as any other job, so it can wait on SQL Server, ERP, or file transfer jobs running on-premises.
What Azure credentials does JAMS need?
A Tenant ID, Client ID, Client Secret, and Subscription ID. The client secret is stored as a JAMS credential, and the rest go on an Azure connection in the Connection Store.
Which JAMS version added Azure Data Factory?
JAMS 7.8.1. Both the JAMS Scheduler and the JAMS Client need to be on 7.8.1 or later.
