1. Large-Scale IoT Telemetry Ingestion for ML with Azure Event Hubs


    To set up a large-scale IoT telemetry ingestion pipeline using Azure Event Hubs, you will need to create the following resources:

    1. Event Hubs Namespace - A container that provides a unique scoping environment for your Event Hub instances. It's where you create one or more Event Hubs.
    2. Event Hub - An ingestion point for your telemetry data. Devices and services can send data to the Event Hub, and downstream processes or analytics systems consume this data.
    3. Event Hubs Authorization Rule - Defines access policies for the resources within your Event Hubs namespace to control permissions.

    We'll be leveraging azure-native provider packages which enable us to interact with Azure services natively using Pulumi.

    Below is a Pulumi program written in Python that will set up the necessary infrastructure for IoT telemetry ingestion using Azure Event Hubs.

    Program Explanation

    • We start by importing the required modules.
    • We use EventHubNamespace from the azure-native.eventhub package to create the namespace.
    • Next, an EventHub instance is created within the namespace.
    • An EventHubAuthorizationRule is created to enable access to the Event Hub.
    • Finally, we export the primary connection string of the Event Hub's authorization rule. This connection string can be used by your IoT devices and services to securely send telemetry data to Azure Event Hubs.

    Here's how the program looks like:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('iot-telemetry-rg') # Create an Azure Event Hubs Namespace event_hub_namespace = azure_native.eventhub.Namespace('telemetry-namespace', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.eventhub.SkuArgs( name="Standard", # Choose 'Basic' or 'Standard' as per your requirements )) # Create an Azure Event Hub within the Namespace event_hub = azure_native.eventhub.EventHub('telemetry-hub', resource_group_name=resource_group.name, namespace_name=event_hub_namespace.name, partition_count=4, # Choose the number of partitions based on volume and scale requirements message_retention_in_days=2 # Number of days to retain the events ) # Create an Authorization Rule for the Event Hub to manage access rights auth_rule = azure_native.eventhub.EventHubAuthorizationRule('telemetry-hub-auth-rule', resource_group_name=resource_group.name, namespace_name=event_hub_namespace.name, event_hub_name=event_hub.name, rights=['Send', 'Listen']) # Permissions for sending and listening to events # Get primary connection string for the namespace authorization rule to use with devices/services primary_connection_string = pulumi.Output.all(resource_group.name, event_hub_namespace.name, auth_rule.name).apply( lambda args: azure_native.eventhub.list_namespace_keys(ListNamespaceKeysArgs( resource_group_name=args[0], namespace_name=args[1], authorization_rule_name=args[2] )).apply(lambda keys: keys.primary_connection_string)) # Export the connection string of the Event Hub pulumi.export('primary_connection_string', primary_connection_string)

    With the above Pulumi program, an infrastructure, as shown, will enable you to ingest large amounts of telemetry data from numerous IoT devices. This data can be consumed by various services for real-time analytics or passed to a machine learning model for predictions and further insights.

    For a more advanced setup, you can integrate further resources, such as Azure Stream Analytics for real-time data stream processing or Azure Machine Learning to build and train machine learning models using the ingested telemetry data.