1. Connecting Azure Databricks to Azure Cosmos DB for AI Workloads


    To connect Azure Databricks to Azure Cosmos DB for AI Workloads, you typically need to create both an Azure Databricks workspace and an Azure Cosmos DB account. Once these are set up, you can use the Azure Databricks workspace to run analytics and AI workloads while using the Azure Cosmos DB as your data source.

    Here is how you would use Pulumi to create both the Azure Databricks workspace and Azure Cosmos DB account:

    1. Azure Databricks Workspace: This is an Apache Spark-based analytics platform optimized for Azure. You need this for running heavy analytics and machine learning workloads.

    2. Azure Cosmos DB Account: This is a globally distributed, multi-model database service. It provides the storage and retrieval of data for your AI workloads.

    Let's proceed with the code that sets up both services. I'll also include comments to explain what each part of the code is doing.

    import pulumi import pulumi_azure_native as azure_native # Define the resource group where our resources will live. resource_group = azure_native.resources.ResourceGroup("ai_resource_group") # Create an Azure Databricks Workspace. databricks_workspace = azure_native.databricks.Workspace( "ai_databricks_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.databricks.WorkspaceSkuArgs( name="Standard" # Choose between "Standard", "Premium", or "Trial". ), managed_resource_group_id=pulumi.Output.concat("/subscriptions/", pulumi.get_stack(), "/resourceGroups/", resource_group.name, "_databricks") ) # Create an Azure Cosmos DB account. cosmos_db_account = azure_native.documentdb.DatabaseAccount( "ai_cosmos_db_account", resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", consistency_policy=azure_native.documentdb.ConsistencyPolicyArgs( default_consistency_level="Session", # Can be "BoundedStaleness", "ConsistentPrefix", "Eventual", or "Strong". ), locations=[azure_native.documentdb.LocationArgs( location_name=resource_group.location, failover_priority=0, )], ) # Export the Azure Databricks Workspace URL and the Azure Cosmos DB account's endpoint. pulumi.export("databricks_workspace_url", databricks_workspace.workspace_url) pulumi.export("cosmos_db_account_endpoint", cosmos_db_account.document_endpoint)

    In this code, we:

    • Import the necessary Pulumi modules.
    • Set up a new Azure resource group to contain our services.
    • Create an Azure Databricks Workspace, specifying the SKU as "Standard". There are different SKU options available like "Standard", "Premium" and "Trial"; you can select based on your requirements.
    • Create an Azure Cosmos DB account. We have set the consistency level to "Session", which is a common choice for scenarios that require a balance between read scalability, data freshness, and transactional consistency.
    • Export the URL endpoint of the Azure Databricks workspace and the endpoint of the Azure Cosmos DB account, which you can use to connect to these services from your applications.

    To connect these services for use in AI workloads, you typically need to use the information exported by the code (like the Databricks workspace URL and the Cosmos DB account endpoint) in your application or data processing jobs. You may also need to implement network security rules to ensure secure access, which can be set up using additional Pulumi resources.