1. Scheduled Data Transformation Workflows in Databricks

    Python

    To create scheduled data transformation workflows in Databricks using Pulumi, you would use a combination of Databricks resources such as databricks.Job, databricks.Cluster, and databricks.Notebook. Together, these resources allow you to define your data processing logic, the computational environment where it will run, and the schedule that will trigger the job.

    Here's a breakdown of these resources to help you understand how they work together:

    • databricks.Cluster: This Pulumi resource represents a Databricks cluster. It allows you to configure the size, type, and other settings related to compute instances that run your data processing tasks.

    • databricks.Notebook: The databricks.Notebook resource represents a Databricks notebook, which contains the code for data transformation workflows written in languages that Databricks supports, such as Python, Scala, SQL, or R.

    • databricks.Job: Finally, the databricks.Job resource represents a Databricks job. You can associate a notebook or a JAR with a job and set a schedule (using a CRON expression) to run it periodically. It also allows you to set up alerts and notifications based on the job outcome.

    Below is a Pulumi program in Python that sets up a simple scheduled data transformation workflow in Databricks:

    import pulumi import pulumi_databricks as databricks # Here, we create a new Databricks cluster where our data transformation will occur. # The number of workers, node type, and other configurations can be set according to the # resource requirements of your transformation job. cluster = databricks.Cluster("transformation-cluster", num_workers=2, node_type_id="Standard_DS3_v2", spark_version="7.3.x-scala2.12", # Set to the desired Spark Version autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=3, ) ) # Now we define a Databricks notebook resource. This notebook contains the data transformation logic. # The content of the notebook is typically defined in a source file (e.g., a .dbc or .py file). notebook = databricks.Notebook("transformation-notebook", path="/Workspace/path/to/notebook", content_base64="base64-encoded-content" ) # Finally, we define the Databricks job that schedules and runs the data transformation workflow. # We provide it with the notebook and cluster we defined earlier and set up a schedule for it. job = databricks.Job("scheduled-transformation-job", existing_cluster_id=cluster.id, notebook_task=databricks.JobNotebookTaskArgs( notebook_path=notebook.path, ), # Set the schedule in CRON format. For example, this job runs at 5 AM every day. schedule=databricks.JobScheduleArgs( quartz_cron_expression="0 0 5 * * ?", timezone_id="America/Los_Angeles" ) ) # Finally, exporting the URLs or IDs that can be used to interact with or query the status of the resources pulumi.export("cluster_id", cluster.id) pulumi.export("notebook_path", notebook.path) pulumi.export("job_id", job.id)

    To summarize the steps in the program:

    1. We create a Databricks cluster with the desired configuration to run our data jobs.
    2. We configure a notebook in Databricks with our data transformation logic.
    3. We create a scheduled job that references the cluster and the notebook, and we set the schedule using the CRON expression and timezone.

    This Pulumi program must be deployed using the Pulumi CLI, and you will also need to have a Pulumi account and the Databricks provider configured with the appropriate credentials. The resources will then be created in your Databricks workspace, ready to process and transform data as scheduled.


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