1. Centralized Settings for Consistent Databricks SQL Environments


    To manage centralized settings for consistent Databricks SQL environments using Pulumi, you would typically use the appropriate Databricks resources. These resources enable you to define global configurations for your SQL environment, such as security policies, SQL configuration parameters, and data access configurations. The goal is to ensure that your Databricks environments are consistently configured in an automated and repeatable manner.

    In this particular scenario, we would utilize the databricks.SqlGlobalConfig resource, among others, depending on the precise requirements. This resource allows you to set up configurations that are applied across all SQL endpoints within the Databricks workspace.

    Below is the Python program written with Pulumi that demonstrates how to set up centralized settings for Databricks SQL environments. It includes the SqlGlobalConfig and also sets up an example table and cluster resource to show how you might use these resources to create a consistent SQL environment.

    import pulumi import pulumi_databricks as databricks # Define global SQL configuration settings for your Databricks SQL environment. # Refer to https://www.pulumi.com/registry/packages/databricks/api-docs/sqlglobalconfig/ for detailed documentation. global_sql_config = databricks.SqlGlobalConfig("global-sql-config", # Define the necessary global settings for SQL configuration. # This could include security policies, instance profiles, and SQL parameters. # Replace the settings below with the ones applicable for your environment. security_policy="CUSTOM", sql_config_params={ "key1": "value1", "key2": "value2", }, instance_profile_arn="arn:aws:iam::123456789012:instance-profile/MyProfile", google_service_account="my-service-account@appspot.gserviceaccount.com" ) # Create a table within the Databricks workspace. # This shows how to use the 'Table' resource to ensure a consistent schema setup as part of your SQL environment. # Refer to https://www.pulumi.com/registry/packages/databricks/api-docs/table/ for detailed documentation. example_table = databricks.Table("example-table", # Define the table's properties. Every table must have a name, owner, columns, etc. name="my_table", owner="owner_id", columns=[ { "name": "id", "typeText": "INT", "position": 0, "typeName": "integer", }, { "name": "data", "typeText": "STRING", "position": 1, "typeName": "string", }, ], tableType="MANAGED", schemaName="sample_schema", catalogName="sample_catalog", dataSourceFormat="delta", ) # Define a Databricks cluster configuration which could be standardized across your organization. # This shows how you can set up consistent cluster settings using the 'Cluster' resource. # Refer to https://www.pulumi.com/registry/packages/databricks/api-docs/cluster/ for detailed documentation. example_cluster = databricks.Cluster("example-cluster", # Standard settings for clusters across your Databricks SQL environments. # Settings include node type, spark version, and autoscaling parameters if necessary. nodeTypeId="i3.xlarge", sparkVersion="7.3.x-scala2.12", autoscale={ "minWorkers": 1, "maxWorkers": 5, }, # You might also include shared libraries, custom tags, and other configurations here. ) # Exports are useful to output the IDs or URLs of the created resources. # These exports can be consumed by other Pulumi programs or for reference purposes. pulumi.export("global_sql_config_id", global_sql_config.id) pulumi.export("example_table_id", example_table.id) pulumi.export("example_cluster_id", example_cluster.id)

    In the program above:

    • The databricks.SqlGlobalConfig resource is used to enforce a security policy and set global SQL parameters.
    • The databricks.Table resource defines a table with a specific schema and format, ensuring consistency for newly created tables or managing the schema for existing ones.
    • The databricks.Cluster resource is an example of defining a cluster configuration with specific properties like node type and scaling parameters.

    These are the basic building blocks you can use to set up centralized settings for your Databricks SQL environments. You can extend this example by adding or modifying configurations and resources to better fit the requirements of your setup.