1. MongoDB Atlas Data Lake for AI-driven Analytics


    If you're looking to use MongoDB Atlas with Pulumi for AI-driven analytics, you'll likely want to set up a data lake where you can store and analyze large amounts of varied data. MongoDB's Atlas Data Lake service allows you to query data using the MongoDB Query Language (MQL) across AWS S3 buckets, which is ideal for analytics workflows.

    Here's how you can set up a MongoDB Atlas Data Lake in Pulumi:

    1. Set up a MongoDB Atlas Project: Before you can create a data lake, you need a project within MongoDB Atlas where the data lake will reside.
    2. Configure the Data Lake: Provision a data lake attached to your MongoDB Atlas project, set the data sources, and configure any additional settings necessary for analytics.

    The provided code will guide you through setting up a simple MongoDB Atlas Data Lake. Make sure you've set up your Pulumi environment and have the necessary MongoDB Atlas and AWS credentials configured.

    Pulumi Program for MongoDB Atlas Data Lake

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Replace these variables with your own specific values project_id = "your_atlas_project_id" aws_role_id = "your_aws_role_id" aws_external_id = "your_aws_external_id" s3_bucket_name = "your_s3_bucket_name" datalake_name = "your_datalake_name" # MongoDB Atlas Data Lake requires AWS credentials to access the S3 bucket aws_credentials = mongodbatlas.DataLakeAwsArgs( role_id=aws_role_id, external_id=aws_external_id, test_s3_bucket=s3_bucket_name ) # Configure MongoDB Atlas Data Lake # This sets up the Data Lake to use AWS S3 as its storage back end and processes data in a specific region mongo_datalake = mongodbatlas.DataLake( datalake_name, name=datalake_name, data_process_region=mongodbatlas.DataLakeDataProcessRegionArgs( cloud_provider="AWS", region="us-east-1" ), aws=aws_credentials, project_id=project_id ) # Export a relevant attribute of the datalake - this will give you the AWS IAM role ARN pulumi.export('datalake_aws_role_arn', mongo_datalake.aws.apply(lambda aws: aws.role_id))


    • The DataLakeAwsArgs object defines the relationship between your AWS environment and the MongoDB Atlas Data Lake. Notably, the role_id and external_id are part of a role assumption process for secure cross-account access to the S3 bucket.

    • The DataLake resource provisions a new data lake in MongoDB Atlas. It references the AWS credentials set up earlier and the MongoDB Atlas project ID where your data lake should reside.

    • The data_process_region specifies in which region the data processing should take place. This should ideally be the same region where your AWS S3 buckets are located to minimize latency.

    • Finally, we use pulumi.export to output the AWS IAM role ARN, which is necessary if you want to programmatically reference or manage the AWS IAM role outside of Pulumi.

    Next Steps

    After deploying this Pulumi program, your data lake will be set up, but it won't have any data sources configured. You'll need to configure your data lake with the specific S3 buckets or paths you want to query within your analytics workloads.

    In the context of AI-driven analytics, you’ll typically connect this data lake to analytic tools or platforms that can process the data for machine learning, business intelligence, or other data-intensive tasks.

    Additionally, you might also want to implement access control, integrate with other analytics services, and set up automated data processing pipelines depending on your analytical requirements.