1. High-throughput Network Setup for Azure Databricks AI


    To create a high-throughput network setup for Azure Databricks AI, we need to provision several resources that facilitate a performant and secure network environment for the Azure Databricks workspace. Here's what we'll achieve with the Pulumi code:

    1. Azure Databricks Workspace: This is the central resource for all Databricks assets. It will allow data scientists and engineers to collaborate on shared projects with high-performance analytics capabilities.

    2. Virtual Network Peering: By setting up virtual network peering, the Databricks workspace can communicate securely with other Azure services or on-premises infrastructure without traffic routing through the public internet.

    3. Private Endpoint: This enables private access to the Databricks workspace from within the Azure virtual network, enhancing the security of your data by ensuring it stays on the Microsoft network.

    4. Access Connector: Databricks access connectors ensure secure connections with other Azure resources or services, allowing configurations that may facilitate higher throughput and performance.

    The following Pulumi program will set up these required resources in Azure using Python. This Pulumi program is simplified to demonstrate the essential resources and should be adapted based on specific requirements such as network addressing, security rules, and resources existing in the environment.

    import pulumi import pulumi_azure_native as azure_native # Pulumi stack configuration for resource names and location config = pulumi.Config() resource_group_name = config.require("resourceGroupName") location = config.require("location") # Create an Azure resource group if it doesn't exist resource_group = azure_native.resources.ResourceGroup("resource_group", resource_group_name=resource_group_name) # Provision an Azure Databricks Workspace databricks_workspace = azure_native.databricks.Workspace("databricks_workspace", resource_group_name=resource_group.name, location=location, sku=azure_native.databricks.SkuArgs(name="standard"), tags={"Environment": "Production"}) # Configure a Virtual Network for Databricks Workspace vnet = azure_native.network.VirtualNetwork("databricks_vnet", resource_group_name=resource_group.name, location=location, # Define a suitable address space for your VNet address_space=azure_native.network.AddressSpaceArgs( address_prefixes=[""] ), subnets=[azure_native.network.SubnetArgs( name="default", address_prefix="", # By enabling service endpoints, the subnet has access to Azure services over the network service_endpoints=[azure_native.network.ServiceEndpointPropertiesFormatArgs( service="Microsoft.Databricks" )] )]) # Create a private endpoint for Databricks private_endpoint = azure_native.network.PrivateEndpoint("databricks_private_endpoint", resource_group_name=resource_group.name, location=location, private_link_service_connections=[azure_native.network.PrivateLinkServiceConnectionArgs( name="databricks_private_link_connection", private_link_service_id=databricks_workspace.id, group_ids=["Workspace"] )], subnet=azure_native.network.SubnetArgs( id=vnet.subnets.apply(lambda subnets: subnets[0].id) )) # Access Connector to establish secure connections between Azure Databricks and other services access_connector = azure_native.databricks.AccessConnector("databricks_access_connector", resource_group_name=resource_group.name, location=location, identity=azure_native.databricks.IdentityArgs( type="SystemAssigned" )) # Outputs for important resource properties and URLs pulumi.export("DatabricksWorkspaceUrl", databricks_workspace.workspace_url) pulumi.export("PrivateEndpointId", private_endpoint.id) pulumi.export("AccessConnectorId", access_connector.id)

    Before running this program, you must ensure the Pulumi CLI is installed and configured for Azure.

    To execute the Pulumi program:

    1. Save the code to a file (__main__.py) in a new directory.
    2. Create a new Pulumi.yaml and Pulumi.<stack-name>.yaml to define the stack and configuration.
    3. Navigate to the directory in the command line.
    4. Run pulumi up to preview and create the resources.

    Please adapt the IP ranges and names where necessary, and consider adding security rules and roles according to your organizational policies.