1. Global Configuration Management for Databricks SQL Workloads


    In order to set up global configuration management for Databricks SQL workloads, we would use the databricks.SqlGlobalConfig resource. This resource allows you to configure global properties for SQL endpoints in your Databricks workspace. These properties may include security policies, SQL configuration parameters, instance profiles, Google service accounts, and more.

    Below, I'll provide a detailed explanation and a Pulumi program in Python that sets up a global configuration for Databricks SQL workloads:


    • Resource: We'll use the databricks.SqlGlobalConfig resource from the pulumi_databricks package to define our global configurations.
    • Security Policy: This could be a string representing a JSON object that specifies the security policy for the SQL endpoints.
    • SQL Configuration Parameters: This is where you can set various SQL parameters that will be applied globally to all SQL workloads.
    • Instance Profiles: AWS instance profile ARNs that will be used by the SQL endpoints for accessing other AWS services.
    • Google Service Accounts: If you're running Databricks in a GCP environment, this is where you would specify your Google service accounts.

    Let's proceed to write the Pulumi program that applies these settings in a Databricks environment.

    import pulumi import pulumi_databricks as databricks # Create a new Databricks SQL Global Configuration sql_global_config = databricks.SqlGlobalConfig("global-config", # Replace the value with the appropriate JSON string that represents your security policy security_policy="{\"key\": \"value\"}", # SQL configuration parameters in a JSON-like format (this should be replaced with parameters relevant to your use case) sql_config_params={ "param1": "value1", "param2": "value2" }, # The instance profile ARN (this should be replaced with the actual ARN you wish to use) instance_profile_arn="arn:aws:iam::123456789012:instance-profile/SQLAccessProfile", # The Google service account (this should be replaced with the actual account you wish to use) google_service_account="your-service-account@gcp-project.iam.gserviceaccount.com", # Optionally, enable or disable serverless compute for your SQL workloads enable_serverless_compute=True ) # Export the ID of the SQL global configuration pulumi.export("sql_global_config_id", sql_global_config.id)

    Further Information

    • Replace placeholders like {\"key\": \"value\"} with the actual settings you need for your security policy.
    • Replace "param1": "value1", "param2": "value2" with the SQL parameters as per your requirements.

    In the above code, sql_global_config is the resource we are creating. All SQL workloads within your Databricks environment will inherit settings from this global configuration. This ensures consistency across your SQL workloads and simplifies management by centralizing these settings.

    For more information on each of the properties and the databricks.SqlGlobalConfig resource, please refer to the Databricks provider documentation.

    Before running this program, ensure you have the Pulumi CLI installed, and you are logged into the Databricks cloud environment through Pulumi so that the program can create resources in your Databricks workspace.

    By running pulumi up, Pulumi will perform the deployment, applying the global configurations specified in the program to your Databricks environment.