1. Scalable BI Semantic Models for AI Workloads on Azure


    To create a scalable BI semantic model for AI workloads on Azure, you'll need to leverage Azure Analysis Services, which allow you to build semantic data models with the scale, performance, and ease of use needed for your BI, analytics, and reporting needs. This is done by creating an Analysis Services server with Pulumi and Azure Native.

    Below is a Pulumi program in Python that represents the creation of an Azure Analysis Services server with a sample configuration that can be scaled according to your workload requirements. This server will be the foundation of your BI semantic model.

    Here's a step-by-step guide along with the Pulumi program:

    1. Import the required modules - we import both the pulumi and the necessary azure-native modules.

    2. Create a Resource Group - Azure encourages the use of resource groups to organize and manage the resources. We create a new resource group into which our resources will be deployed.

    3. Create an Analysis Services Server - This server will be used to host your BI semantic models. You specify the SKU (Stock Keeping Unit) based on your performance and scale requirements. Here, the 'S1' SKU implies that it is from the standard tier, and the '1' implies that you want to provision 1 query replica. You can adjust this based on your needs.

    4. Configuration - You can include additional configuration like tags, administrators, and an array of backupBlobContainerUris if you want to back up your Analysis Services databases to blob storage.

    5. Export the Server Name - The server name is exported as an output for you to use, such as when connecting to it from Power BI or other tools.

    Now, let's look at the Pulumi program:

    import pulumi import pulumi_azure_native.resources as resources import pulumi_azure_native.analysisservices as analysisservices # Create an Azure Resource Group resource_group = resources.ResourceGroup("resource_group") # Create an Azure Analysis Services Server analysis_services_server = analysisservices.Server( "analysisServicesServer", resource_group_name=resource_group.name, sku=analysisservices.SkuArgs( name="S1", # You can choose from different SKUs depending on your requirements tier="Standard" ), # Additional optional configurations: # tags={"environment": "production"}, # administrators=["user@example.com"], # backup_blob_container_uri=["https://storagaccount.blob.core.windows.net/backups"] ) # Export the Analysis Services Server name pulumi.export("analysisServicesServerName", analysis_services_server.name)

    This simple Pulumi Python program sets up a scalable BI semantic model host environment for AI workloads. You can add more configurations into this program based on your requirements, such as setting up data sources, integrating with Azure Machine Learning services, and configuring data refresh schedules.

    The Analysis Services' scalability allows you to adjust resources as needed to accommodate the varying performance demands of your AI workloads. You'll also want to ensure that you have appropriate monitoring in place to measure the performance of your BI semantic models, and potentially use automation to scale resources in response to demand.

    Remember to replace placeholder values, such as user@example.com and https://storagaccount.blob.core.windows.net/backups, with actual values before deployment. Additionally, you'll need the Azure CLI installed and logged in, and Pulumi CLI setup with an active Pulumi stack before running this code.