Using azure appinsights with databricks

Question

Pulumi · Accepted Answer

Integrating Azure Application Insights with Azure Databricks allows you to monitor your Databricks cluster and applications in real-time, track custom events, and understand the performance and reliability of your applications. Application Insights is an Azure Monitor feature. To achieve this integration within a Pulumi script, we'll create both the Databricks Workspace and the Application Insights resources.

We use the `azure-native.databricks.Workspace` resource to create an Azure Databricks workspace. Additionally, we make use of Application Insights resources from the `azure-native.insights` namespace, specifically `Component` which represents an instance of Application Insights.

Here's how you can set this up in TypeScript using Pulumi:

```typescript
import * as pulumi from "@pulumi/pulumi";
import * as azure from "@pulumi/azure-native";

// Create an Azure Resource Group
const resourceGroup = new azure.resources.ResourceGroup("resourceGroup");

// Create an Azure Databricks Workspace
const workspace = new azure.databricks.Workspace("workspace", {
    resourceGroupName: resourceGroup.name,
    location: resourceGroup.location,
    sku: {
        name: "standard", // Choose an appropriate SKU for your Workspace
    },
});

// Create an Azure Application Insights component
const appInsights = new azure.insights.Component("appInsights", {
    resourceGroupName: resourceGroup.name,
    kind: "web", // This kind (web) is typically used for web applications
    applicationType: "web",
    location: resourceGroup.location,
});

// Export the Instrumentation Key of Application Insights
// This key is used to integrate with the Databricks application/workload
export const instrumentationKey = appInsights.instrumentationKey;

// Output the Azure Databricks Workspace URL
// You'll use this URL to access the Azure Databricks environment
export const databricksWorkspaceUrl = pulumi.interpolate`https://${workspace.workspaceUrl}`;

```

In the above program:

- We start by creating an Azure Resource Group, which is a container for all resources related to a specific application or project.
- Then, we create the Azure Databricks workspace within this resource group. The SKU is set to 'standard', but depending on your needs, you might want to choose a different tier.
- We also create an Application Insights instance, which is useful for monitoring applications. We specify the kind as 'web', which is commonly used for back-end services.
- After creating the Application Insights component, we export its Instrumentation Key, which you can use in your Databricks notebooks or jobs for sending telemetry to Application Insights.
- Finally, we export the Databricks Workspace URL so you can navigate to your Databricks workspace through the web.

To apply this Pulumi program:

1. Save the above code to a file named `index.ts`.
2. Run `pulumi up` in the same directory as the `index.ts` file to create the resources.
3. After the resources are created, Pulumi CLI will output the Instrumentation Key and the Azure Databricks Workspace URL.

Please replace `"standard"` with the appropriate SKU for your Databricks Workspace if necessary. For integrating with Application Insights, you would typically apply the Instrumentation Key in your Databricks notebooks or application configuration, according to the Application Insights SDK documentation relevant to your workload.