1. Cross-Project Data Aggregation for AI Insights on MongoDB Atlas


    Cross-project data aggregation in MongoDB Atlas using Pulumi involves orchestrating different MongoDB Atlas resources to create a unified data environment that can be used for AI insights. The primary resource we'll engage is the mongodbatlas.Cluster, which will help us set up a MongoDB cluster that houses our data.

    We'll create a MongoDB Atlas cluster within a specific project and set up auditing for transparency and compliance. The next step typically involves configuring your application to use MongoDB's aggregation pipeline to run cross-data analyses, but this falls outside the realm of infrastructure setup and into application development, which we won't cover here.

    Below is a Pulumi program that sets up a MongoDB Atlas project, cluster, and enables auditing. This is done using the pulumi_mongodbatlas package, which is a Pulumi provider for MongoDB Atlas. Ensure that you have the proper MongoDB Atlas API keys configured for Pulumi to authenticate your requests.

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Set up a MongoDB Atlas project. project = mongodbatlas.Project("my-project", # Add required configs such as org_id. org_id="your-org-id", # The name attribute is optional. If you don't specify it, Pulumi auto-generates a unique name. ) # Set up a MongoDB Atlas cluster in the project above. cluster = mongodbatlas.Cluster("my-cluster", project_id=project.id, # Required properties for provisioning a MongoDB Cluster: cluster_type="REPLICASET", mongo_db_major_version="4.4", name="my-cluster", # Cluster name used in MongoDB, must be unique per project. provider_backup_enabled=True, # Use MongoDB's Cloud Backup service. provider_disk_iops=100, # Input/Output operations per second. provider_encrypt_ebs_volume=True, # Encrypt EBS volume if using AWS. provider_instance_size_name="M10", # Set the instance size (varies by provider). provider_region_name="US_EAST_1", # Set the region where the cluster will be created. replication_factor=3, # Number of replica set members. replication_specs=[{ 'region_configs': [ { 'region_name': 'US_EAST_1', 'electable_nodes': 3, 'priority': 7, 'read_only_nodes': 0, }, ], }], ) # Set up auditing for the created project. auditing = mongodbatlas.Auditing("my-auditing", project_id=project.id, audit_filter='{"atype": {"$in": ["authCheck", "createCollection", "dropCollection"]}}', enabled=True, ) # Export outputs for easy access. pulumi.export('project_id', project.id) pulumi.export('cluster_id', cluster.id) pulumi.export('auditing', auditing.id)

    This program accomplishes the following:

    1. Initializes a new MongoDB Atlas project within your organization.
    2. Deploys a MongoDB cluster with specifications such as instance size, region, and backup configurations suitable for AI workloads.
    3. Auditing is enabled for the project to collect logs on specific data management events which are crucial for debugging and compliance.

    It is important to add additional configurations and resources relevant to your workload, such as users, network peering, encryption at rest, and others to secure and optimize your infrastructure for AI processing.

    Please note that AI insights and data aggregation logic will be part of your application code, leveraging MongoDB's data aggregation framework and possibly other toolsets for processing and machine learning.

    Remember that this code should be run in an environment where Pulumi is already installed and configured for MongoDB Atlas access, and be sure to replace placeholder values "your-org-id" with your organization's actual ID and other placeholder values as per your requirements.