1. Serverless Data Preprocessing with Azure Functions


    Serverless data preprocessing using Azure Functions is a powerful way to handle data transformation tasks without provisioning or managing servers. Azure Functions allows you to execute code in response to events on a variety of Azure services, HTTP requests, and webhook events.

    In the context of Pulumi, you can declare your Azure Functions and related resources using infrastructure as code, which provides repeatable deployment processes and simplified management.

    Here, I'll guide you through a program written in Python using Pulumi that sets up a serverless data preprocessing pipeline using Azure Functions. The function will be triggered by HTTP requests, but you can configure other triggers as needed.

    First, we'll need to create an Azure Function App, which is the logical container for our functions, along with a corresponding storage account which is required by the Function App to operate. For the Function App, we can specify settings like the runtime and version, application insights for monitoring, and any application settings as key-value pairs.

    Let's begin with the code:

    import pulumi from pulumi_azure_native import storage, web, insights # Create a resource group resource_group = resources.ResourceGroup("resource_group") # Create an Azure Storage Account for the Function App storage_account = storage.StorageAccount("storageaccount", resource_group_name=resource_group.name, location=resource_group.location, sku=storage.SkuArgs(name=storage.SkuName.STANDARD_LRS), kind=storage.Kind.STORAGE_V2) # Create an App Insights for monitoring app_insights = insights.Component("appinsights", resource_group_name=resource_group.name, location=resource_group.location, application_type=insights.ApplicationType.WEB, kind="web") # Create an Azure Function App function_app = web.WebApp("functionapp", resource_group_name=resource_group.name, location=resource_group.location, server_farm_id=service_plan.id, site_config=web.SiteConfigArgs(app_settings=[ web.NameValuePairArgs(name="FUNCTIONS_WORKER_RUNTIME", value="python"), # worker runtime is language-specific web.NameValuePairArgs(name="FUNCTIONS_EXTENSION_VERSION", value="~3"), web.NameValuePairArgs(name="APPINSIGHTS_INSTRUMENTATIONKEY", value=app_insights.instrumentation_key) ]), https_only=True, identity=web.ManagedServiceIdentityArgs(type="SystemAssigned")) # Assign a consumption plan to the Function App service_plan = web.AppServicePlan("serviceplan", resource_group_name=resource_group.name, location=resource_group.location, kind="FunctionApp", sku=web.SkuDescriptionArgs( name="Y1", tier="Dynamic" )) # Output the function app url pulumi.export("function_app_default_hostname", function_app.default_host_name)

    In this program, we:

    • Create a resource group called resource_group to manage all related Azure resources.
    • Set up an Azure storage account storageaccount, which is required by the Function App for its internal operations like managing function execution and state.
    • Provision an Application Insights instance appinsights dedicated to monitoring the performance and effectiveness of our Function App. This step is optional but recommended for production workloads.
    • Define the Azure Function App itself functionapp, which includes a set of application settings that are required to run Python functions in Azure Functions.
    • Attach an App Service Plan serviceplan to our Function App which dictates how our functions are hosted and how resources are allocated. Here, we use a dynamic consumption plan (name: "Y1"), which automatically allocates and scales resources on-demand, and you only pay for the compute resources your functions use while they are running.
    • Finally, we export the default hostname of our Function App, which you can use to interact with your deployed HTTP-triggered functions.

    This Pulumi program allows you to deploy the infrastructure necessary to host Azure Functions, which can then be used for serverless data preprocessing tasks. To deploy this code using Pulumi, you need to have the Pulumi CLI installed and configured for Azure.

    Once the Pulumi program is executed, you can deploy your Python-based serverless functions by packaging your function code and deploying it to the Function App using tools like the Azure Functions Core Tools or directly from your preferred CI/CD pipeline.