Controlling Access to AI Data Warehouses in Databricks
PythonControlling 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
- 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. - Databricks Table: We then define a table using the
databricks.Table
resource, specifying schema information, storage location, and other properties. - 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 grantREAD_WRITE
permissions to a user with the usernamedata_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.
- Databricks Cluster: We create a Databricks cluster which will be used to run our data analysis jobs. The cluster is defined using the