1. Real-time Data Ingestion for Azure AI Event-Driven Models


    To set up a real-time data ingestion pipeline on Azure for AI event-driven models, we'll need to create a couple of Azure resources using Pulumi—an Event Hub for ingesting the real-time data, followed by a Stream Analytics Job to process the data, and finally integrating it with Azure's AI services for building event-driven models.

    Here's what each service does in our setup:

    • Azure Event Hub: Serves as the entry point for data. It's a highly scalable data streaming platform and event ingestion service capable of receiving and processing millions of events per second.
    • Azure Stream Analytics Job: Processes the data from Event Hub in real-time. It can transform and analyze the stream of data and trigger actions or further processing, such as feeding into AI models for scoring or classification.

    Below is an example program written in Python using Pulumi to create these resources. I'll explain each piece of the program step by step.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure Event Hub Namespace, which is required for the Event Hub. eventhub_namespace = azure_native.eventhub.Namespace("eventhub_namespace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.eventhub.SkuArgs( name="Standard", # Standard tier is generally suitable for AI-driven workloads. ), ) # Create the Event Hub where the events will be sent. event_hub = azure_native.eventhub.EventHub("event_hub", namespace_name=eventhub_namespace.name, resource_group_name=resource_group.name, partition_count=4, # Number of partitions for data isolation and parallelism. ) # Create a Stream Analytics Job to process the data in real-time. stream_analytics_job = azure_native.streamanalytics.StreamingJob("stream_analytics_job", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.streamanalytics.SkuArgs( name="Standard", # This aligns with the chosen Event Hub tier. ), events_out_of_order_policy="Adjust", # Adjusts for out-of-order events based on a timestamp. output_error_policy="Drop", # Drops event if there's an error during output. events_out_of_order_max_delay_in_seconds=5, # A 5-second delay for out-of-order event handling. # The streaming units determine the amount of data that can be processed. More units can be added based on the load. streaming_units=3, ) # We now need to define the input to the Stream Analytics Job which is the Event Hub created earlier. eventhub_input = azure_native.streamanalytics.Input("eventhub_input", resource_group_name=resource_group.name, job_name=stream_analytics_job.name, properties=azure_native.streamanalytics.ReferenceInputPropertiesArgs( datasource=azure_native.streamanalytics.ReferenceInputDataSourceArgs( type="Microsoft.StreamAnalytics/StreamingJobs/Inputs/EventHub", properties=azure_native.streamanalytics.EventHubStreamInputDataSourcePropertiesArgs( eventhub_name=event_hub.name, service_bus_namespace=eventhub_namespace.name, shared_access_policy_name="RootManageSharedAccessKey", shared_access_policy_key=eventhub_namespace.default_primary_key, ), ), serialization=azure_native.streamanalytics.JsonSerializationArgs( type="Json", properties=azure_native.streamanalytics.JsonSerializationPropertiesArgs( encoding="UTF8", ), ), ), ) # Outputs and integration with AI models can be defined similarly, or an output to another Event Hub/Service Bus Topic, etc. # To export the Event Hub name and the Stream Analytics Job name for later reference or integration. pulumi.export('event_hub_name', event_hub.name) pulumi.export('stream_analytics_job_name', stream_analytics_job.name)

    This Pulumi program starts by creating an Azure resource group, which is a container that holds related resources for an Azure solution. In Azure, the Event Hub requires a namespace, so we create one under the Standard tier for suitable performance.

    The Event Hub is then created within this namespace. It is configured with a number of partitions for parallel processing. For real-time processing, we set up a Stream Analytics Job to process the data coming from the Event Hub. We specify the job settings such as the policy for out-of-order events, the error policy, and the number of streaming units which determines the capacity for data processing.

    We then create an input source for the Stream Analytics Job, which is the Event Hub we previously set up, to pull in the real-time event data. The ReferenceInputPropertiesArgs is used to define the properties of our event source, including details such as the Event Hub name, namespace, and shared access policy.

    Finally, we export the names of the Event Hub and Stream Analytics Job as stack outputs. These outputs can be used for integration with other services like Azure Machine Learning models or any other external applications that needs to process the data.