1. Collaborative Data Science Workspaces with Databricks Repos


    Setting up a collaborative data science workspace involves several components, but the focus here is on the Databricks platform, which is a unified data analytics platform for data science and engineering. Within Databricks, you can utilize Repos to manage and collaborate on code development for data science.

    Pulumi provides resources to programmatically define and manage these workspaces, which can streamline the setup process.

    Let's go through a simple example of how to set up a collaborative workspace using Pulumi and the Databricks provider. We'll assume the Databricks Workspace is already in place and focus on configuring a Repo within it.

    In our program, we'll perform the following steps:

    1. Import the necessary Databricks package.
    2. Set up a Databricks Repo, which serves as a Git repository within the Databricks workspace where your data science team can commit their notebooks and code.

    Below is a Pulumi program that creates a Databricks Repo. Before you use the following program, you should have the Pulumi CLI installed, Databricks workspace configured, and the appropriate permissions set to interact with Databricks resources through Pulumi.

    import pulumi import pulumi_databricks as databricks # Instantiate a new Databricks Repo. You will need to substitute the placeholders with # actual values that point to your Git repository, your Databricks workspace, and the desired branch. repo = databricks.Repo("data-science-repo", # Replace '<url>' with your repository URL. url="<url>", # Substitute '<path>' with the desired filesystem path within the Databricks Workspace path="<path>", # Provide the branch name you want to synchronize with. branch="main" # You can also specify commitHash, gitProvider, and other parameters if needed. ) # Export the Repo URL which can be used to access the Databricks Repo. pulumi.export('repo_url', repo.url)


    1. Import Statements: We import the Pulumi SDK for Python to write our infrastructure as code and the Databricks provider package to interact with Databricks resources.

    2. Databricks Repo: The databricks.Repo resource is used to create a new Repo within the Databricks Workspace. The url parameter specifies the location of the Git repository that will be used. The path is the filesystem path for the repo within the Databricks Workspace where the code will live. Finally, the branch parameter is used to point to the main branch in the repository. You can set other configurations like commitHash or gitProvider based on your requirements.

    3. Exports: The pulumi.export function makes it possible to output the URL of the repo so that it can be easily accessed or passed to other services or team members as needed.

    Before deploying this code with Pulumi, make sure to replace the placeholders (<url> and <path>) with actual values that are specific to your environment.

    This program provides a starting point for setting up Databricks Repos, which you can expand upon to integrate more sophisticated features like handling Pull Requests, setting up CI/CD pipelines for your data science code, and adding access controls within the Workspace.