Managing SSH Access to Azure Databricks for Data Scientists

Question

Pulumi · Accepted Answer

To manage SSH access to an Azure Databricks workspace for your team of data scientists, there are several steps involved. You'll use Pulumi, an infrastructure as code tool, to provision and configure the necessary Azure resources programmatically.

The following program performs these tasks:
1. Provisions an Azure Databricks workspace using the `azure-native.databricks.Workspace` resource which is the central piece of infrastructure for running data analytics workflows and machine learning experiments.
2. Configures access control using the `AccessControl` property where you can specify permissions for the data scientists.

Below is a Pulumi program written in Python to achieve this:

```python
import pulumi
import pulumi_azure_native as azure_native

# Provision an Azure Databricks workspace.
databricks_workspace = azure_native.databricks.Workspace("dataScientistsWorkspace",
    resource_group_name="myResourceGroup",
    workspace_name="myDatabricksWorkspace",
    location="westus",
    sku=azure_native.databricks.SkuArgs(
        name="standard",  # Choose from among 'standard', 'premium', or 'trial'
    ),
    tags={
        "Environment": "Dev",
    }
)

# Assuming your data scientists are represented by Azure Active Directory objects,
# you can configure access control at creation as shown in this example modification.
data_scientist_role_assignment = azure_native.authorization.RoleAssignment("dataScientistRoleAssignment",
    principal_id="00000000-0000-0000-0000-000000000000",  # Replace with the actual principal ID of the user or service principal.
    role_definition_id="/subscriptions/{subscriptionId}/providers/Microsoft.Authorization/roleDefinitions/00000000-0000-0000-0000-000000000000",  # Replace with the actual role definition ID that corresponds to data scientist permissions.
    scope=databricks_workspace.id,
)

# Export the workspace URL, which your data scientists can use to access the Databricks workspace.
pulumi.export('Databricks Workspace URL:', databricks_workspace.workspace_url)
```

### Explanation:

- `azure_native.databricks.Workspace` creates a new Azure Databricks workspace. The workspace is the fundamental resource for running data processing jobs and hosting notebooks. The `sku` determines the level of capabilities available in the workspace.

- The `tags` dictionary includes user-defined tags which can help you organize and identify your resources.

- `azure_native.authorization.RoleAssignment` assigns a role to a principal (like a user, group, or service principal) for access control. You'll need to replace the placeholder IDs in `principal_id` and `role_definition_id` with actual values corresponding to your user or group of data scientists and a role that matches the permissions needed.

- `pulumi.export` outputs the URL of the Databricks workspace so you know what to access once the provisioning is complete.

- The workspace location is set to "West US", but you should choose a location close to your user base for better performance.

This program represents the basic pattern for setting up an Azure Databricks workspace and managing SSH access for users. You can expand upon this with more complex access controls, networking configurations, and workspace parameters as required for your specific use case.