Dynamic AI Data Pipeline Access Control with MongoDB Atlas Cloud Provider Access

Question

Pulumi · Accepted Answer

Creating a dynamic AI data pipeline typically involves working with various data sources, processing units, and storage services. In this context, you may need a MongoDB Atlas database to store and retrieve the data used and generated by the AI models. To ensure fine-grained access control to your MongoDB Atlas resources from a cloud provider like AWS or Azure, you would use MongoDB Atlas's Cloud Provider Access features.

MongoDB Atlas Cloud Provider Access allows you to set up and manage the integration between a cloud provider's IAM (Identity and Access Management) service and MongoDB Atlas. With this setup, you can manage access permissions in a more centralized and standardized way, leveraging the cloud provider's IAM roles and policies.

Below, we will write a Pulumi program that sets up access control using MongoDB Atlas Cloud Provider Access. This program will illustrate how to:

1. Provision the necessary MongoDB Atlas resources using the `mongodbatlas` provider.
2. Configure the Cloud Provider Access to enable AWS IAM roles to access MongoDB Atlas.

To do this, we will use two main resources:
- `CloudProviderAccess`, which represents a mechanism to manage the cloud provider access within MongoDB Atlas.
- `CloudProviderAccessSetup`, which helps configure the access to the cloud provider roles.

Before you begin, you need to have Pulumi installed and configured with the necessary cloud provider credentials. Additionally, ensure that you have an account with MongoDB Atlas and the API keys to interact with its resources.

```python
import pulumi
import pulumi_mongodbatlas as mongodbatlas

# Configure your MongoDB Atlas project ID.
project_id = 'your-mongo-atlas-project-id'

# Set up the cloud provider access. In this example, we integrate with AWS.
cloud_provider_access = mongodbatlas.CloudProviderAccess(
    "cloudProviderAccess",
    project_id=project_id,
    provider_name="AWS", # Specify "AWS" as the provider.
)

# For Azure, replace provider_name="AWS" with provider_name="AZURE", and provide necessary Azure-specific arguments.
# To complete the setup, you must authorize the Atlas-managed IAM roles in your cloud provider.
# The setup resource returns the IAM role information that you will use to create an IAM role.
cloud_provider_access_setup = mongodbatlas.CloudProviderAccessSetup(
    "cloudProviderAccessSetup",
    project_id=project_id,
    provider_name=cloud_provider_access.provider_name,
    # If you're setting up Azure, you'll need to provide the `azureConfigs` argument instead of `awsConfigs`.
    aws_configs=[{
        "iam_assumed_role_arn": cloud_provider_access_setup.aws_iam_assumed_role_arn,
        # This is the assumed IAM role ARN that MongoDB Atlas will use.
    }],
)

pulumi.export('cloudProviderAccessId', cloud_provider_access.id)
pulumi.export('awsIamRoleId', cloud_provider_access_setup.aws_iam_role_id)
```

In this program:

- We provision the `CloudProviderAccess` resource, specifying that we want to integrate with AWS.
- The `CloudProviderAccessSetup` resource specifies the access setup necessary for the integration. Once created, it provides the ARN (Amazon Resource Name) of the IAM role that MongoDB Atlas will assume.
- We export the cloud provider access ID and the AWS IAM Role ID to be used in other parts of our Pulumi program or for reference in the AWS and MongoDB Atlas consoles.

To apply this program, run `pulumi up` from the command line. Pulumi will execute the infrastructure code you've defined, provisioning the necessary resources on MongoDB Atlas.

Keep in mind that this is a simplified example focusing on integrating MongoDB Atlas Cloud Provider Access with AWS. Depending on your requirements, you might need to configure additional parameters or resources.