Can you run Databricks CLI script from the Databricks workspace?

Yes, you can definitely run Databricks CLI scripts from within your Azure Databricks workspace! This can be a convenient and efficient way to automate tasks and workflows without requiring an additional Azure VM.

Here are two ways to run Databricks CLI scripts from your workspace:
  1. Using Databricks notebooks:
    • Create a new notebook: Within your workspace, click on "Workspace" in the left pane and then select "Create Notebook".
    • Paste your script: Open a new cell in the notebook and paste your Databricks CLI script into it.
    • Run the script: Click the "Run Cell" button next to the cell containing your script.
    Creating a new notebook in Azure Databricks workspace
  2. Using the "Run Job" feature:
    • Access the Jobs panel: From the workspace menu, click on "Jobs" in the left pane.
    • Create a new job: Click on "Create Job" and select "Spark Job" as the job type.
    • Configure the job: In the job configuration, choose "Run Now" as the schedule and enter a name for your job.
    • Add script as a library: Under Libraries, click on "Add Library" and choose "Local Jar/File" as the source. Specify the path to your Databricks CLI script file within your workspace.
    • Submit the job: Click on "Submit" to run your script as a Spark job.
    Creating a new Spark Job in Azure Databricks workspace

Benefits of running CLI scripts from the workspace:

  • Convenience: No need to manage a separate VM for script execution.
  • Integration: Leverage Databricks resources, libraries, and security features within your workspace.
  • Scalability: Utilize the power of the Databricks cluster for efficient script execution.
  • Job scheduling: Schedule your scripts to run automatically at specific times or intervals.

Things to consider:

  • Permissions: Ensure your Databricks user account has the necessary permissions to run CLI commands within the workspace.
  • Script format: Make sure your script is compatible with the Databricks CLI environment and uses the appropriate command syntax.
  • Error handling: Implement proper error handling and logging mechanisms within your script to diagnose and troubleshoot any issues.

Remember to choose the method that best suits your script complexity, execution frequency, and desired level of control. Feel free to ask if you have any further questions about specific scripting tasks or need help adapting your existing script for execution within your Databricks workspace!