1. Collaborative Data Science on Databricks Workspaces

    Python

    To facilitate collaborative data science using Databricks Workspaces, we will be creating a Databricks workspace on Azure using Pulumi's infrastructure as code tools. Databricks provides a collaborative environment with a shared workspace where data scientists can write code, analyze data, and build machine learning models. On Azure, Databricks is integrated with Azure Active Directory, Blob Storage, and other Azure services to enable secure and efficient data science workflows.

    In this program, we will use the azure-native.databricks.Workspace resource provided by Pulumi to create a new Databricks workspace. This resource allows for the provisioning and configuration of a Databricks workspace within Azure, which can be customized based on the requirements of your team or organization.

    We will go through the following steps to set up a collaborative Databricks workspace:

    1. Provision a new Databricks workspace on Azure.
    2. Set up the required configurations such as the location, SKU for pricing tier, and the resource group where the workspace will reside.
    3. Export the URL of the newly created Databricks workspace to easily access it.

    Below is a Pulumi program written in Python that accomplishes these tasks:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group if you don't have one already resource_group = azure_native.resources.ResourceGroup("databricksResourceGroup") # Create an Azure Databricks workspace databricks_workspace = azure_native.databricks.Workspace( "databricksWorkspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.databricks.WorkspaceSkuArgs( name="standard" # choose from "standard", "premium", or other available SKUs based on your needs ), # Add additional configurations if needed, such as enabling encryption, virtual network settings, etc. ) # Export the Databricks workspace URL pulumi.export('databricks_workspace_url', databricks_workspace.workspace_url)

    In this program, we:

    • Import the necessary pulumi modules for Azure.
    • Create a resource group if you don't have one. The resource group serves as a container that holds related resources for an Azure solution. In this example, we name it databricksResourceGroup.
    • Provision the Databricks workspace with azure_native.databricks.Workspace in the previously created resource group and specify the location to match the resource group location.
    • The sku parameter specifies which pricing tier to use for the workspace. SKUs dictate capabilities and pricing; "standard" is used here, but your choice depends on your requirements and budget.
    • Finally, we export the URL of the Databricks workspace, which can be used to access the Databricks workspace in a web browser.

    After running this program with Pulumi, you will have a fully operational Databricks workspace hosted on Azure where your data science team can collaborate. You can also further configure this workspace for specific needs, such as setting up Azure Blob Storage for data storage, configuring virtual networks for enhanced security, and integrating other Azure services.


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