1. Secure Collaboration with Databricks Shared Workspaces


    When you want to set up secure collaboration with Databricks Shared Workspaces on the cloud, you make use of various resources that are specified by cloud providers like AWS, Azure, or GCP and Databricks itself. The goal is to configure these workspaces so that multiple users or teams can work together on shared data projects, notebooks, and experiments while ensuring that access is controlled and data is protected.

    In this context, let's consider that you are using AWS as your cloud provider and you want to deploy an infrastructure that supports this type of collaboration via Pulumi in Python. We'll go through the process step by step.

    Step 1: Setting up the Databricks Workspace

    The first step is to set up a Databricks workspace. A workspace is an environment for accessing all of your Databricks assets. The workspace organizes objects (notebooks, libraries, and experiments) into folders and provides access to data and computational resources.

    To set up a Databricks workspace, we'll use the databricks.MwsWorkspaces resource. This resource is responsible for deploying and managing the lifecycle of a Databricks workspace.

    Step 2: Peering VPCs for Secure Access

    For secure collaboration, the next step involves setting up networking that allows users to communicate with the Databricks workspace securely. We could use the azure.databricks.VirtualNetworkPeering or a similar AWS resource, depending on the exact requirements.

    VPC Peering allows your Databricks workspace to communicate with other virtual networks securely without exposing your traffic to the public internet.

    Step 3: Configuring Data Sharing

    To share data securely between Databricks workspaces, you use the databricks.Metastore resource. This allows you to manage data sharing between Databricks workspaces, making it possible for multiple teams to collaborate on the same datasets without risking data leaks or unauthorized access.

    Step 4: Assign and Manage Access Rights

    To manage the access rights, you need to set up permissions and access control lists (ACLs). Databricks has built-in support for this, and you would typically use the Databricks UI or CLI to set up these permissions.

    Now, let's see how the implementation in Pulumi might look like.

    import pulumi import pulumi_aws as aws import pulumi_databricks as databricks # Step 1: Provision an AWS Databricks workspace # Please replace placeholders with actual values workspace = databricks.MwsWorkspaces("databricksWorkspace", workspace_name="my-databricks-workspace", pricing_tier="standard", aws_region="us-west-2", # Assuming AWS credentials and specific workspace characteristics are already set up appropriately ) # Step 2: Set up networking for secure access—this example assumes the networking is already established # If you need to create new VPCs or subnets you would use the aws.ec2.Vpc and aws.ec2.Subnet resources # Step 3: Set up a Metastore for sharing data between workspaces metastore = databricks.Metastore("sharedMetastore", name="shared-metastore", storage_root="s3://my-shared-metastore-bucket/", # Assumed that an S3 bucket 'my-shared-metastore-bucket' is already created and configured with proper access policies ) # Step 4: IAM roles and policies would need to be set up through AWS to manage access rights # This typically involves creating roles and attaching policies that specify the allowed actions within the workspace # Export the workspace URL for easy access pulumi.export("workspaceUrl", workspace.workspace_url) # Export the metastore ID so it can be referenced by other workspaces pulumi.export("metastoreId", metastore.metastore_id)

    What this program does is it sets up a Databricks Workspace on AWS with a shared metastore for data sharing. It assumes pre-existing networking configurations and IAM roles/policies—these would have to be set up as well, potentially using other Pulumi resources or via the AWS and Databricks consoles depending on the specific controls and policies your organization requires.

    This is a simplified implementation, and in a real-world scenario, one may need to configure additional resources and policies for things like data encryption, networking restrictions, and more nuanced access controls.

    Keep in mind that Pulumi resources can reflect most, but not all, of what you would configure in the Databricks and AWS consoles, and some configurations, especially around detailed workspace settings and user/role management, may need to be done directly through the Databricks UI or CLI.

    Remember to consult the official Pulumi Databricks provider documentation for more detailed information on the resources, properties, and methods available.