1. Secure Credentials Management for AI Pipelines with Databricks SecretScope


    Managing credentials securely is crucial for AI pipelines, particularly when using cloud services like Databricks. Pulumi enables you to provision and manage cloud infrastructure using code, which includes creating and managing secrets in a secure manner.

    In the context of Databricks, secret scopes are used to store secrets like database connection strings, API keys, or any sensitive information that your application might need at runtime but you don't want to expose in your code or version control system.

    Below is a Pulumi program in Python that demonstrates how you could use the Databricks provider to create a SecretScope, and then add a secret to it. The program sets up the required Databricks provider, then uses the SecretScope and Secret resources to manage the credentials.

    Note that you'll need to have Databricks configured in your environment to apply these resources. That typically means having the appropriate Databricks credentials in your local environment or passed through the Pulumi configuration.

    import pulumi import pulumi_databricks as databricks # Create a SecretScope in Databricks. Secret scopes allow you to store secrets, # such as passwords and access keys, safely. Each secret is a key-value pair. # Please ensure you have the Databricks provider configured. secret_scope = databricks.SecretScope("my-secret-scope", # Initial_manage_principal refers to whom initially has the MANAGE permission. # By default, this is users, which means that users who have the ability to # create secret scopes can manage the secrets within it. You can also set # this to "creator" to mean only the creator of the secret scope can manage # it. Here we'll assume you're jumping in as the admin/creator. initial_manage_principal="creator", # Optionally, you can provide a description for your secret scope. description="This SecretScope contains secrets for AI Pipelines" ) # Add a secret within the created SecretScope. # Here for the sake of example, let's store an API key that your AI pipeline # will use to interact with an external service. api_key_secret = databricks.Secret("my-api-key", key="api-key", # The key (or the name) of the secret string_value="supersecretkey", # The string value of the secret scope=secret_scope.name # Reference to the SecretScope's name ) # Export the name of the secret scope pulumi.export('secret_scope_name', secret_scope.name)

    In this program:

    • We import the Pulumi and Pulumi Databricks packages so we can work with these cloud resources.
    • We define a SecretScope resource named my-secret-scope that will hold our secrets.
    • We define a secret with the name my-api-key which holds a string value supersecretkey. This is just an example, and in a real-world scenario, you would likely have your secrets stored in a secure store and reference them here, not hard-coded.
    • Lastly, we export the secret_scope_name which is the name assigned to the Databricks Secret Scope upon creation. This is just an identifier and does not expose any secret values.

    The string_value of the secret is where the actual secret resides, and this should come from a secure location during runtime or from Pulumi's configuration system.

    Before using this code, you'll need to ensure that:

    • You have installed the required Pulumi Databricks provider package. You can install it using pip install pulumi_databricks.
    • You are authenticated with Databricks. Typically, this would be through the Databricks CLI, with credentials configured.
    • You have set the Pulumi stack configurations required for Databricks, including host and token.

    When this Pulumi program is run, it will provision these resources in your Databricks account. The secret will be available for use in your AI pipelines without having to expose the sensitive data in your source code or configuration files.