1. AI-powered Application Telemetry with Azure Application Insights

    Python

    Creating an AI-powered Application Telemetry setup in Azure involves setting up an instance of Azure Application Insights, which is an extensible Application Performance Management (APM) service for developers and DevOps professionals. It allows you to monitor live applications, automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues and to understand what users actually do with your app.

    Azure Application Insights can collect telemetry from your application at any scale, supports multiple platforms including .NET, Node.js, Java, and Python, and integrates with your DevOps processes. It also offers intelligent analytics and proactive insights thanks to its AI capabilities.

    Below, you will find a Pulumi program written in Python that sets up Azure Application Insights for an application. This program defines the necessary resources using the azure-native provider, which is the Pulumi provider for Azure Resource Manager (ARM), enabling us to define infrastructure through Azure's native deployment model.

    Here's what we will accomplish in the program:

    1. Import necessary libraries: We'll include the Pulumi SDK and the Azure provider to interact with Azure services.
    2. Create an instance of Azure Insights: This is the main component that will collect telemetry data from your application.
    3. Set up other required resources: Depending on the needs, you might also need to define other resources like an Azure Resource Group or a data collection endpoint.
    4. Export outputs: These are essential values that you might need to use outside the Pulumi program, such as the instrumentation key you'll need to configure your application to send data to Application Insights.
    import pulumi from pulumi_azure_native import insights, resources # Create an Azure Resource Group resource_group = resources.ResourceGroup("ai-resource-group") # Create an Application Insights component app_insights = insights.Component("appInsightsComponent", resource_group_name=resource_group.name, kind="web", # The kind of Application Insights component. "web" is for web applications. location=resource_group.location, # Use the location of the resource group for the Application Insights component. application_type="web" # Type of application being monitored. ) # Export the Instrumentation Key, you'll need to add this key to your application's configuration so that telemetry will be sent to Application Insights. pulumi.export('instrumentation_key', app_insights.instrumentation_key)

    This program first creates an Azure Resource Group, ai-resource-group, which is a container that holds related resources for an Azure solution. In this case, it is used to group the Application Insights resources.

    Next, we create the Application Insights component by instantiating insights.Component. We set the kind to 'web', indicating this instance is for monitoring web applications, and the application_type is also set to 'web' for clarity. The location is set to be the same as that of the resource group, ensuring everything is provisioned to the same region.

    Finally, we export the instrumentation key from the Application Insights component because you'll need it to configure your application so that it can send telemetry data to Application Insights.

    Remember, to run this Pulumi program, you'll need to have the Pulumi CLI installed and configured for Azure, and you would execute pulumi up to provision the resources defined in the code. After the deployment, Pulumi will output the instrumentation key, which you can then use to configure your application.


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