1. Large-Scale Data Engineering for AI on Azure Databricks


    To set up a large-scale data engineering environment for AI on Azure, using Azure Databricks is a natural choice. Azure Databricks is an analytics platform optimized for the Microsoft Azure cloud services platform. It provides a collaborative environment with a workspace for developing data engineering as well as data science projects.

    To harness the power of Azure Databricks for data engineering, we'll need to provision a few resources:

    1. Azure Databricks Workspace: This is the foundational resource for Azure Databricks, providing a collaborative space where data professionals can work on various data and AI tasks. The workspace manages the configuration of the Databricks runtime environment and allows for the creation of notebooks and experiments.

    2. Managed Resource Group: Azure Databricks requires an Azure resource group where it can manage its own resources. This includes resources such as networks, storage, and compute that are necessary for running Databricks jobs.

    Here's how you can create an Azure Databricks workspace resource using Pulumi with Python. The following program will set up an Azure Databricks workspace with the necessary configurations for large-scale data engineering tasks:

    import pulumi import pulumi_azure_native as azure_native # Here we initialize the Pulumi program by creating an Azure Resource Group where all other resources will reside. resource_group = azure_native.resources.ResourceGroup("ai_data_eng_resource_group") # This dict represents the settings of the Databricks workspace SKU. We're generally interested in the standard or premium tier for large-scale data engineering. sku_args = azure_native.databricks.SkuArgs( name="standard", # Sku name: choose between 'standard', 'premium', or other appropriate SKUs for your needs. ) # Now, we create the Databricks Workspace itself. # We assign it to the previously created resource group and specify the SKU. databricks_workspace = azure_native.databricks.Workspace( "ai_data_eng_databricks_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=sku_args, tags={"Environment": "Large-Scale Data Engineering"}, # Add tags as necessary to categorize and manage your resources. ) # Expose the Databricks Workspace URL as an output of our Pulumi program. # This URL is used to access the Databricks workspace environment where you will orchestrate your data engineering pipelines. pulumi.export("databricks_workspace_url", databricks_workspace.workspace_url)

    Key Components of this program:

    • We begin by importing the necessary Pulumi modules to interact with Azure.
    • We create a new instance of ResourceGroup to logically group related resources for data engineering in one place.
    • Next, we declare the Azure Databricks workspace within the resource group and specify the geographical location for deployment.
    • The sku_args defines the level of service for the workspace — a necessary consideration for performance and cost management. Your choice of SKU (e.g., standard, premium) will depend on the specific needs of your data engineering processes.
    • Finally, we expose the workspace URL as an output. This URL is crucial for developers and data engineers to access the Databricks platform.

    With this Pulumi program, running pulumi up will provision the defined resources on Azure, establishing a foundational workspace where data teams can start building out their data pipelines and performing machine learning experiments.