1. Secure Access to Data Lakes for AI with Databricks


    In order to secure access to Data Lakes for AI with Databricks using Pulumi, you would require to configure a couple of things:

    1. A Databricks workspace where you can run AI models.
    2. Properly configured storage (like an S3 bucket, Azure Data Lake Storage, or Google Cloud Storage) depending on your cloud provider.
    3. IAM roles or service principals with fine-grained access controls to ensure that only authorized Databricks clusters have access to the data lake.

    For the sake of this explanation, we're going to assume you're using AWS as your cloud provider, but similar concepts apply for Azure and GCP with their respective services and authorization mechanisms.

    First, let's start by setting up a Databricks cluster that will be used to run your AI models. We'll need to:

    • Create a secret scope and secret to securely store S3 access credentials.
    • Establish a Databricks cluster configuration where these credentials are used, ensuring that only this cluster can access your data lake.

    Here's how you might write a Pulumi program in Python to accomplish this:

    import pulumi import pulumi_databricks as databricks # Be sure to configure your AWS provider and credentials separately. # Define a secret scope named 'ai_datalake_secrets' where you can store your AWS credentials secret_scope = databricks.SecretScope("ai_datalake_secrets", # Secret scopes are needed to secure and manage secrets like keys or tokens initial_manage_principal="users", # initial_manage_principal allows you to specify who can manage the scope ) # Add AWS access key to the secret scope access_key_secret = databricks.Secret("access_key_secret", key="aws_access_key_id", string_value="your-aws-access-key-id", # replace with your real AWS access key scope=secret_scope.name, ) # Add AWS secret key to the secret scope secret_key_secret = databricks.Secret("secret_key_secret", key="aws_secret_access_key", string_value="your-aws-secret-access-key", # replace with your real AWS secret key scope=secret_scope.name, ) # Define a Databricks cluster that uses the secrets databricks_cluster = databricks.Cluster("ai_databricks_cluster", cluster_name="ai-cluster", spark_version="latest-spark-version", # replace with the latest Spark version you want to use node_type_id="your-instance-type", # replace with the machine type you want to use autotermination_minutes=20, # automatically terminate an inactive cluster after 20 minutes aws_attributes=databricks.ClusterAwsAttributesArgs( # These attributes connect the Databricks workspace to AWS instance_profile_arn="your-instance-profile-arn", # ARN for the instance profile with permissions to S3 zone_id="us-west-2a", # The availability zone ), spark_env_vars={ "AWS_ACCESS_KEY_ID": databricks.get_secret(key=access_key_secret.key, scope=secret_scope.name).apply(lambda secret: secret.value), "AWS_SECRET_ACCESS_KEY": databricks.get_secret(key=secret_key_secret.key, scope=secret_scope.name).apply(lambda secret: secret.value), # Use the .apply() method to pass secret values as environment variables }, # ... configure additional cluster details as needed ... ) # Export the cluster ID pulumi.export("cluster_id", databricks_cluster.cluster_id)

    In the above program:

    • We create a secret scope named ai_datalake_secrets, which is a logical grouping of secrets. This enables you to secure the credentials needed to access AWS S3.
    • Within the scope, we store AWS credentials. Please replace "your-aws-access-key-id" and "your-aws-secret-access-key" with your real AWS access and secret keys.
    • We create a Databricks cluster configured to use AWS credentials stored in the secret scope. The "AWS_ACCESS_KEY_ID" and "AWS_SECRET_ACCESS_KEY" environment variables are set from the secrets to securely provide access to the AWS resources.
    • We export the cluster ID as an output of our Pulumi program for reference. Outputs are useful for getting information about the infrastructure to use elsewhere, further programmatically, or for other operational purposes.

    To tie this into your Data Lake and ensure secure access:

    • Set up permissions on your S3 buckets (or other storage) to allow access only to the instance profile used by the Databricks cluster. You can configure these with AWS resource blocks like aws_iam_policy and aws_iam_role_policy_attachment if you're using the pulumi_aws package.
    • Consider encrypting your data at rest within the Data Lake using AWS's S3 bucket encryption settings.
    • Ensure secure network communication by setting up VPCs, subnets, and security groups that allow traffic only from the Databricks workspace.

    Remember, while this program sets up a basic secure environment, you must still follow best practices and comply with your organization's policies to fully protect your Data Lakes and AI workloads.