Control-M for Azure Databricks

Azure Databricks is a cloud-based data analytics platform that enables you to process large workloads of data.

Control-M for Azure Databricks enables you to do the following:

• Connect to any Azure Databricks workspace using service principal authentication.
• Integrate Azure Databricks jobs with other Control-M jobs into a single scheduling environment.
• Monitor the Azure Databricks workspace status and view the results in the Monitoring domain.
• Attach an SLA job to your entire Azure Databricks data service.
• Introduce all Control-M capabilities to Azure Databricks, including advanced scheduling criteria, complex dependencies, quantitative and control resources, and variables.
• Run 50 Azure Databricks jobs simultaneously per Control-M/Agent.

Control-M for Azure Databricks Compatibility

The following table lists the prerequisites that are required to use the Azure Databricks plug-in, each with its minimum required version.

Control-M for Azure Databricks is supported on Control-M Web and Control-M Automation API, but not on Control-M client.

To download the required installation files for each prerequisite, see Obtaining Control-M Installation Files.

Setting up Control-M for Azure Databricks

This procedure describes how to deploy the Azure Databricks plug-in, create a connection profile, and define an Azure Databricks job in Control-M Web and Automation API.

NOTE: Integration plug-ins released by BMC require an Application Integrator installation at your site. However, these plug-ins are not editable and you cannot import them into Application Integrator. To deploy these integrations to your Control-M environment, you import them directly into Control-M using Control-M Automation API.

Before you Begin

Verify that Automation API is installed, as described in Automation API Installation.

Begin

1. Create a temporary directory to save the downloaded files.

2. Click http://www.bmc.com/available/epd and follow the instructions on the EPD site to download the Azure Databricks plug-in from the Control-M Integrations page, or go directly to the Control-M for Azure Databricks download page.

3. Deploy the Azure Databricks job via Automation API, as described in deploy jobtype.
4. Create an Azure Databricks connection profile in Control-M Web or Automation API, as follows:
   • Web: Creating a Centralized Connection Profile with Azure Databricks Connection Profile Parameters
   • Automation API:  ConnectionProfile:Azure Databricks
5. Define an Azure Databricks job in Control-M Web or Automation API, as follows:
   • Web: Create a Job and then define specific Azure Databricks parameters in Azure Databricks Job parameters.
   • Automation API: Job:Azure Databricks