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 from a single computer with secure login, which eliminates the need to provide 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 Application Integrator||188.8.131.52|
|Control-M Automation API||184.108.40.206|
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 via EPD.
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.
Create a temporary directory to save the downloaded files.
- Deploy the Azure Databricks job via Automation API, as described in deploy jobtype.
- Create an Azure Databricks connection profile in Control-M Web or Automation API, as follows:
- Define an Azure Databricks job in Control-M Web or Automation API, as follows: