1. Real-Time AI Analytics with Azure Databricks Event Hubs.


    To set up real-time AI analytics with Azure Databricks and Event Hubs, you'll need to create an Azure Databricks workspace, which is an environment for stream processing and machine learning. Azure Event Hubs is a data streaming platform and event ingestion service, which can collect, process, and store event streams.

    In the context of real-time AI analytics, Azure Databricks can consume data from Azure Event Hubs, process it using its analytics engine, and apply machine learning models for tasks like real-time predictions, anomaly detection, etc.

    Below is a complete Pulumi program written in Python that sets up Azure Databricks and connects it with an Azure Event Hub. The program will:

    1. Create an Event Hub namespace, which is a container for Event Hubs.
    2. Create an Event Hub within the namespace.
    3. Set up an Azure Databricks workspace.
    4. Create an event hub data connection from Databricks to consume the event stream from the Event Hub.

    Here's the detailed Pulumi code:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group to contain our infrastructure resource_group = azure_native.resources.ResourceGroup("my-resource-group") # Create an Event Hubs Namespace for the Event Hub event_hub_namespace = azure_native.eventhub.Namespace("my-eventhub-namespace", resource_group_name=resource_group.name, sku=azure_native.eventhub.SkuArgs( name="Standard" # "Standard" tier is required for "Capture" feature used in streaming scenarios ), location=resource_group.location) # Create an Event Hub in the namespace event_hub = azure_native.eventhub.EventHub("my-event-hub", resource_group_name=resource_group.name, namespace_name=event_hub_namespace.name) # Create an Azure Databricks workspace databricks_workspace = azure_native.databricks.Workspace("my-databricks-workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.databricks.SkuArgs( name="Standard" # Chose the Sku that fits your needs ), managed_resource_group_id=f"/subscriptions/{pulumi.config.Config('azure-native').require('subscriptionId')}/resourceGroups/{resource_group.name}_databricks") # Output the Azure Databricks workspace URL which can be used to access the workspace pulumi.export('databricks_workspace_url', pulumi.Output.concat("https://", databricks_workspace.workspace_url)) # Output the Event Hub connection string (needed to connect from Databricks) pulumi.export('event_hub_connection_string', event_hub_namespace.default_secondary_connection_string)

    What does this code do?

    • It sets up a new Azure resource group to hold all related infrastructure.
    • It creates an Event Hubs Namespace that acts as a logical container for one or more Event Hubs.
    • It creates an Event Hub which will be used to ingest the real-time stream data.
    • It provisions an Azure Databricks workspace using the 'Standard' SKU which is suitable for most scenarios.
    • It exports important information such as the Databricks workspace URL and the Event Hub connection string, which are essential for connecting to these services.

    Note: Remember to replace the placeholder {pulumi.config.Config('azure-native').require('subscriptionId')} with your actual Azure Subscription ID.

    To execute this Pulumi program:

    1. Set up the Azure CLI and ensure that you are logged in (az login).
    2. Ensure you have Python 3.x installed.
    3. Install Pulumi CLI.
    4. Create a new Pulumi project and replace the Python code in __main__.py with the above code.
    5. Run pulumi up to preview and deploy the resources.

    This will create the necessary Event Hub and Databricks resources so you can start streaming data and processing it with AI/analytics in Azure.