1. Serverless AI Data Processing with MongoDB Atlas Data Lake


    To create a serverless data processing solution using MongoDB Atlas Data Lake, you will need to set up a Data Lake storage configuration in MongoDB Atlas and ensure you can access this from your serverless functions, typically running on infrastructure like AWS Lambda, Azure Functions, or Google Cloud Functions.

    In Pulumi, you can manage MongoDB Atlas resources using the pulumi_mongodbatlas Python package. The key resource for this configuration is the mongodbatlas.DataLake which allows you to set up a Data Lake in MongoDB Atlas.

    The DataLake resource will be responsible for creating the Data Lake tied to your MongoDB Atlas project. You will need to provide AWS S3 bucket details as the storage backend for this Data Lake since MongoDB Atlas Data Lake integrates with AWS S3 for storing and querying data.

    Below is an example of a Pulumi Python program that sets up a MongoDB Atlas Data Lake. Note that you need an existing MongoDB Atlas project and AWS S3 bucket to use this code. This program does not include the configuration of the serverless functions that would process the data; it just sets up the Data Lake storage.

    Let's walk through the process:

    1. Import the required packages: pulumi_mongodbatlas for MongoDB Atlas resources and pulumi_aws if you're using AWS credentials for the Data Lake backend.

    2. Create an instance of the Data Lake: This will require information like the associated MongoDB Atlas project ID and AWS specifics such as the testS3Bucket where your data resides, roleId, and externalId for establishing access between MongoDB Atlas and your S3 bucket.

    3. Set up the data processing region: This determines where the data processing will take place.

    Here's a program that sets up the MongoDB Atlas Data Lake:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Configure these variables with your own specifics. atlas_project_id = "your_atlas_project_id" aws_role_arn = "your_aws_role_arn" external_id = "your_external_id" # Specify the external ID if you have set one up. s3_bucket_name = "your_s3_bucket_name" # MongoDB Atlas Data Lake which integrates with an AWS S3 bucket. data_lake = mongodbatlas.DataLake("my_data_lake", project_id=atlas_project_id, aws={ "roleId": aws_role_arn, "testS3Bucket": s3_bucket_name, "externalId": external_id, }, data_process_region={ "cloudProvider": "AWS", "region": "us-east-1" } ) # Export the Data Lake ID pulumi.export("data_lake_id", data_lake.id)

    In the above program:

    • We are deploying the MongoDB Atlas Data Lake named my_data_lake.
    • We have specified the associated project ID of the MongoDB Atlas project.
    • We have detailed the AWS specifics, providing the ARN of an AWS IAM role (aws_role_arn) which has the necessary permissions for MongoDB Atlas to access the S3 bucket. And we name the S3 bucket that contains our data (s3_bucket_name).
    • We've also configured the data processing region to be in AWS's us-east-1.

    Please replace your_atlas_project_id, your_aws_role_arn, your_external_id, and your_s3_bucket_name with your actual MongoDB Atlas project ID, your AWS IAM role ARN, an external ID for secure access (if used), and the name of your S3 bucket, respectively.

    To run this program, make sure you have the Pulumi CLI installed and configured for both AWS and MongoDB Atlas access. Once the code is placed into a file (for example, main.py), run pulumi up in the same directory to start the deployment process.