1. Databricks for Collaborative Data Science Workspaces


    Databricks is a powerful analytics platform designed for collaborative data science and engineering. It helps unify data science, engineering, and business by providing a collaborative environment for interactive workspace development, running analytics, and machine learning tasks. When we talk about setting up Databricks for collaborative data science workspaces in a cloud infrastructure context, it typically means provisioning Databricks workspace resources, clusters within those workspaces for computation, storage for notebooks and artifacts, and configuring access control for team collaboration.

    In the context of Pulumi, you would use the Pulumi Databricks provider to create and manage these resources. Below is a Python program that demonstrates how to set up a basic Databricks workspace, cluster, and notebooks using Pulumi for collaborative data science.

    Before we begin, make sure you have installed the pulumi_databricks Python package, which provides the necessary classes and functions to interact with Databricks resources on Pulumi. You can install it using pip:

    pip install pulumi_databricks

    Here's how you would use Pulumi to create a Databricks workspace. Make sure you have the required credentials and configuration for your cloud provider set up correctly before you run this program.

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace workspace = databricks.Workspace("collaborative-workspace", location="westus", sku="standard") # Define a cluster configuration cluster = databricks.Cluster("data-science-cluster", workspace_id=workspace.id, node_type_id="Standard_DS3_v2", spark_version="7.3.x-scala2.12", num_workers=2, autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=10, ) ) # Create a notebook in the workspace notebook = databricks.Notebook("data-analysis", workspace_id=workspace.id, content_base64=pulumi.FileAsset("Data_Analysis.dbc").base64_content, path="/Users/example@example.com/Data Analysis", language=databricks.NotebookLanguage.PYTHON) # Export the workspace URL to access it after it is created pulumi.export("workspace_url", workspace.workspace_url) # Export the cluster ID pulumi.export("cluster_id", cluster.id)

    The above program uses the Pulumi Databricks provider to create a workspace resource and a cluster within that workspace to execute data science and collaborative workflows:

    • Workspace: This is the foundation and serves as a container for all your Databricks assets, including clusters and notebooks. You specify the location and SKU for the workspace. The workspace_url is exported so users can access the workspace through a web browser.

    • Cluster: A cluster is a set of computation resources where you run notebooks and other workloads. Here we define a relatively small cluster using a Cluster resource class and configure it to scale automatically based on the workload with autoscale.

    • Notebook: Represents a Databricks notebook resource, which is an interactive coding environment similar to Jupyter Notebooks. This notebook is uploaded from a local DBC file and placed in a specified directory within the workspace.

    After running this Pulumi program, you'll have a Databricks workspace with a cluster where your team can collaborate on data science projects using notebooks. The URLs to the workspace and cluster ID are exported as stack outputs, which can be accessed by running pulumi stack output.

    Should you have more advanced requirements (e.g., configuring VPCs for secure networking, setting up IAM roles for access controls, integrating with external data sources, or setting specific configurations for clusters), Pulumi's Databricks provider offers resources and options that you can leverage to meet these needs.