1. Controlling Access to AI Data Warehouses in Databricks


    Controlling access to data warehouses, particularly in an AI-driven environment such as Databricks, is crucial to ensure that sensitive data is only accessed by authorized users and systems. With Pulumi, you can manage this access programmatically by defining resources such as permissions, tables, clusters, and secrets that configure access policies and define the data structure itself within Databricks.

    In this program, I will demonstrate how to use Pulumi to set up a Databricks cluster, create a table within a Databricks database, and control access to that table using permissions. This will illustrate one way to manage access control for an AI data warehouse within Databricks.

    Pulumi Program Explanation

    1. Databricks Cluster: We create a Databricks cluster which will be used to run our data analysis jobs. The cluster is defined using the databricks.Cluster resource.
    2. Databricks Table: We then define a table using the databricks.Table resource, specifying schema information, storage location, and other properties.
    3. Databricks Permissions: To control access, we set permissions on the table using the databricks.Permissions resource. Here, we define the access controls, like who has what level of access (read, write, etc.).

    Below is the Python Pulumi program to achieve this:

    import pulumi import pulumi_databricks as databricks # Initialize a Databricks cluster cluster = databricks.Cluster("ai-cluster", spark_version="7.3.x-scala2.12", node_type_id="r3.xlarge", autoscale=databricks.ClusterAutoscaleArgs( max_workers=4, min_workers=2, ), num_workers=2, ) # Define a database table table = databricks.Table("ai-data-table", name="customers", schema_name="default", table_type="MANAGED", columns=[ databricks.TableColumnArgs( name="customer_id", type_name="INT", type_text="INT", nullable=False, position=0, ), databricks.TableColumnArgs( name="customer_name", type_name="STRING", type_text="STRING", nullable=False, position=1, ), # Add more columns as needed ], comment="Table containing customer data for AI analysis.", # Define other properties as needed, like storage location, view definition, etc. ) # Set permissions on the table for a particular user or group permissions = databricks.Permissions("table-permissions", table_id=table.id, access_controls=[databricks.AccessControlArgs( user_name="data_scientist", permission_level="READ_WRITE", )], # Define additional permissions as needed ) # Output the necessary details pulumi.export('cluster_id', cluster.id) pulumi.export('table_name', table.name)

    Let's break down what this program is doing:

    • We use the databricks.Cluster resource to create a scalable Databricks cluster. The autoscaling properties ensure that the cluster can grow or shrink within the specified boundaries to accommodate workload changes.
    • The databricks.Table resource establishes a schema for our data, including the column definitions. This is where your AI data will be stored and queried.
    • The databricks.Permissions resource then applies access controls to the table. In this example, we grant READ_WRITE permissions to a user with the username data_scientist.

    After setting up the program and running it using the Pulumi CLI, the Databricks environment will be configured with a cluster to process AI workloads, a table to store and query data, and proper permissions in place to control access.

    Remember that for this program to execute successfully, you need to have your Databricks and Pulumi accounts set up and configured with the required access rights to create resources in Databricks.