1. Automating Databricks Jobs through Token-Based Rest API Calls


    To automate Databricks jobs through token-based REST API calls using Pulumi, you would need to create a Databricks token that will be used for authentication when making API requests. This is essential as the Databricks REST API requires a valid token for authorization.

    We will use the pulumi_databricks.Token resource to create a new Databricks token. Additionally, we will create Databricks jobs using the pulumi_databricks.Job resource. These jobs can be configured to perform various computations like running notebooks, executing JARs, or submitting SQL commands.

    In the example program below, we follow these steps:

    1. Set up the Databricks provider to connect to a Databricks workspace.
    2. Create a new Databricks token to authenticate API requests.
    3. Define a job in Databricks that could be anything from a notebook task to a Spark JAR task.
    4. Export the necessary information such as the created job's ID and the API token value.

    Before running this Pulumi program, make sure you have the pulumi_databricks provider set up and configured with your Databricks workspace URL and credentials.

    Here is a Pulumi Python program that demonstrates these steps:

    import pulumi import pulumi_databricks as databricks # Configuring the Databricks provider using organization URL and access token. # In actual use, the Databricks token would be fetched from a secret store or environment variable for security. databricks_provider = databricks.Provider("databricks-provider", host="https://<databricks-instance>", # Replace with your Databricks instance URL. token="<databricks-access-token>") # Replace with your actual access token. # Create a Databricks token for job automation. databricks_token = databricks.Token("job-automation-token", comment="Token for job automation") # Define a new Databricks job, for instance, to run a Python script at scheduled intervals. # Replace '<cluster-id>', '<notebook-path>', and '<python-file-name>' with actual values. job = databricks.Job("daily-python-job", existing_cluster_id="<cluster-id>", # Use an existing cluster ID. notebook_task=databricks.JobNotebookTaskArgs( notebook_path="<notebook-path>", # Path to the notebook to run. ), # Schedule the job to run daily. schedule=databricks.JobScheduleArgs( quartz_cron_expression="0 0 0 * * ?", # At 12:00 AM every day. timezone_id="UTC" # Set the timezone to Coordinated Universal Time (UTC). ), opts=pulumi.ResourceOptions(provider=databricks_provider)) # Export the job ID and token value. # In practice, these would be securely passed to wherever they need to be used within your infrastructure. pulumi.export("job_id", job.id) pulumi.export("databricks_token_value", databricks_token.token_value)

    In this program:

    • We start by configuring the Databricks provider with your Databricks workspace instance URL and an access token.
    • Next, we create a new token using databricks.Token. This token will be used for authenticating REST API calls to interact with Databricks.
    • A new job is defined using databricks.Job that points to an existing cluster and a specific notebook to execute. Jobs can be scheduled to run at regular intervals using a cron expression which is set via the schedule argument.
    • Lastly, we export the job ID and token value so they can be used outside of the Pulumi application, such as in CI/CD pipelines or other automation scripts.

    Keep in mind that storing sensitive information like Databricks access tokens should be handled securely using secret stores or environment variables. Pulumi also has its own mechanisms (pulumi.Config) to manage secrets in a safe manner.