1. Collaborative Data Science Workspaces on Databricks


    Creating collaborative data science workspaces in Databricks involves setting up a Databricks workspace and configuring the necessary components such as clusters, notebooks, tables, and permissions for collaborative work. Pulumi provides resources to automate the provisioning and management of these components.

    In the example provided below, we'll create:

    • A Databricks workspace
    • A cluster within that workspace for computation
    • A notebook to allow data scientists to write and execute their code
    • A table to store data

    The databricks.Workspace resource is used to set up the Databricks workspace itself. Once the workspace is set up, a databricks.Cluster resource is created to provide the computational power for running analytics workloads.

    After the computational backbone is in place, a databricks.Notebook resource is created to provide a collaborative environment for writing and sharing Python, Scala, R, or SQL code, which is the core activity of data scientists.

    Finally, with a databricks.Table, we can set up a structure to store and query structured data within the workspace. A table in Databricks is usually a collection of structured data that can be used for things like machine learning, analytics or storage of processed data.

    Let's see a Python program using Pulumi to set all this up.

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace workspace = databricks.Workspace("data-science-workspace", location="westus", sku=databricks.WorkspaceSkuArgs( name="premium" # Choose from: standard, premium, or basic )) # Create a cluster within the Databricks workspace cluster = databricks.Cluster("data-science-cluster", autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, # Minimum number of nodes in the cluster max_workers=5 # Maximum number of nodes in the cluster ), node_type_id="Standard_DS3_v2", # The type of nodes that form the cluster spark_version="6.4.x-scala2.11", # Specifies the runtime and includes the Apache Spark version spark_conf={ "spark.speculation": "true" }) # Create a notebook within the Databricks workspace notebook = databricks.Notebook("data-science-notebook", path="/Shared/data-science-work", language="PYTHON", # Can be set to SCALA, SQL, R, or PYTHON content_base64="VGhpcyBpcyBhIHRlc3Q=") # Base64-encoded string of your notebook source code # Create a table within the Databricks workspace table = databricks.Table("data-science-table", name="user_data", catalog_name=workspace.name, schema_name="default", # Default schema columns=[ databricks.TableColumnArgs( name="id", type_name="INTEGER", nullable=False, position=1 ), databricks.TableColumnArgs( name="name", type_name="STRING", nullable=True, position=2 ) ], table_type="DELTA", # Type of the table: DELTA is a default storage layer in Databricks storage_location="dbfs:/mnt/tables/user_data") # DBFS path where table data is stored # Export the workspace URL to be easily accessible pulumi.export("workspace_url", workspace.workspace_url)

    In this program:

    • We initiate the Pulumi Databricks provider and define the workspace we want to set up. We've selected the premium SKU for more features, but it could be set to standard or basic.
    • We then define a cluster configuration. The node type, the autoscaler settings, and specific Spark configurations can be set according to your workload requirements.
    • Next, we define a Databricks notebook resource, which will be where data scientists code and collaborate.
    • We create a Databricks table resource, which data can be loaded into and manipulated within Databricks SQL or notebooks.

    Finally, the workspace URL is exported using pulumi.export, allowing you to access it easily from the Pulumi console.

    Please note that the content of the notebook is a base64-encoded string. In a real-world scenario, you would likely read this content from a file or another source and encode it appropriately. The cluster definition is also kept simple; in practice, you might use a more sophisticated setup, including private networking, access policies, and data storage options.

    Running the above Pulumi program will provision these resources in your Azure Databricks environment, and you can then start using them for data science work.