Power BI Embedded for Interactive AI Visualizations

Question

Pulumi · Accepted Answer

Power BI Embedded is a Microsoft Azure service that allows developers to embed Power BI visualizations and reports in their applications. This enables users to interact with their data and gain insights seamlessly as part of your application's experience, even if they do not have a Power BI account.

The primary resource for deploying a Power BI Embedded service in Azure using Pulumi is the `AutoScaleVCore` resource from the `azure-native` package. This resource represents an instance of Power BI Embedded that can automatically scale its resources based on the workload.

To provision a Power BI Embedded service using Pulumi, follow these general steps:
1. Define the resource configuration for `AutoScaleVCore`, including the SKU, capacity, and other properties such as the resource group and location.
2. Provision the resource using Pulumi's Azure Native provider.
3. Export any outputs you may need, like the unique identifier for the Power BI resource.

Below is a detailed Pulumi Python program that provisions a Power BI Embedded instance:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create a new resource group to hold the Power BI Embedded resource
resource_group = azure_native.resources.ResourceGroup("resourceGroup")

# Provision a Power BI Embedded instance using the `AutoScaleVCore` resource
power_bi_embedded = azure_native.powerbidedicated.AutoScaleVCore(
    "powerBiEmbedded",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=azure_native.powerbidedicated.Sku(
        name="A1",  # Name of the SKU. "A1" is an example, choose as per your requirement.
        tier="PBIE_Azure",  # Tier of the pricing. Use "PBIE_Azure" for Power BI Embedded.
        capacity=1  # The capacity specification of the SKU.
    ),
    # Additional configurations can be set here.
)

# Export the Power BI Embedded ID as output
pulumi.export("powerBiEmbeddedId", power_bi_embedded.name)
```

In this program, we create an instance of `AutoScaleVCore` under the resource group that we name `resourceGroup`. We specify the location of the Power BI resource to be the same as the resource group's location for easier management and compliance. The SKU is chosen based on your application's requirements, with "A1" being a common entry-level tier. However, you should analyze your needs and pick the SKU that best matches your workload demands.

After running the Pulumi program with `pulumi up`, the Power BI Embedded instance will be provisioned in your Azure subscription. The output `powerBiEmbeddedId` provides the unique identifier of the Power BI resource which can be used in your application for referencing the embedded content.

For more information on Power BI Embedded scaling and configuration options, you can refer to [Azure Native AutoScaleVCore documentation](https://www.pulumi.com/registry/packages/azure-native/api-docs/powerbidedicated/autoscalevcore/).

Remember to review Azure Power BI Embedded pricing details and capacity planning recommendations to choose the right SKU and capacity settings for your workload. These decisions will have an impact on cost and performance of your Power BI Embedded service.