1. Cross-Cloud AI Data Replication with MongoDB Atlas


    To set up cross-cloud AI data replication with MongoDB Atlas, you would typically need to do the following:

    1. Provision a MongoDB Atlas Cluster in your desired cloud provider's region where your AI services will consume data.
    2. Configure your MongoDB Atlas Cluster for cross-region replication to ensure high availability and failover capabilities.
    3. Set up networking in such a way that your AI services in a different cloud provider can securely access the MongoDB Atlas Cluster.

    Below is a Python Pulumi program that demonstrates the setup of a MongoDB Atlas Cluster that is configured for cross-cloud data replication. This cluster will be available to AI services hosted in another cloud provider, assuming those services have appropriate network access.

    In this example, I'll show you how to provision a MongoDB Atlas Cluster with broad parameters suitable for a variety of replication scenarios. Note that you'll need to adjust any configurations for the specific requirements of your project, such as the regions or instance sizes.

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Configure the MongoDB Atlas Provider. # Make sure to configure `mongodbatlas:publicKey` and `mongodbatlas:privateKey` using Pulumi config secrets # before running a `pulumi up` for this program. mongo_provider = mongodbatlas.Provider('my-mongo-provider') # Assume you have MongoDB Atlas project created, and you have the Project ID available. project_id = 'your-atlas-project-id' # Set up a MongoDB Atlas Cluster with replication across multiple regions. # Adjust the `providerName`, `providerRegionName`, and other parameters as needed. cluster = mongodbatlas.Cluster('ai-data-replication-cluster', project_id=project_id, cluster_type="REPLICASET", replication_specs=[mongodbatlas.ClusterReplicationSpecArgs( num_shards=1, regions_configs=[ mongodbatlas.ClusterRegionsConfigArgs( region_name="AWS_US_EAST_1", electable_nodes=3, priority=7, read_only_nodes=0 ), mongodbatlas.ClusterRegionsConfigArgs( region_name="GCP_WEST_1", electable_nodes=2, priority=6, read_only_nodes=0 ), ] )], provider_backup_enabled=True, provider_instance_size_name="M10", provider_name="AWS", mongo_db_major_version="4.2", opts=pulumi.ResourceOptions(provider=mongo_provider) ) # The cluster connection string can be exported to be used by your application. pulumi.export('connection_string', cluster.connection_strings.apply(lambda cs: cs.standard)) # The cluster ID can be useful for integrating with other systems or for management and auditing. pulumi.export('cluster_id', cluster.id)

    The mongodbatlas.Cluster resource initializes a new MongoDB Atlas cluster. The replication_specs configuration is particular to your data replication needs; here, there is a primary region on AWS in US_EAST_1 and a failover replica in GCP's WEST_1 region. The priority setting dictates the failover order; a higher number means a higher priority.

    After you launch this cluster with pulumi up, your MongoDB cluster will be set up with the specified replication configuration. Your application, wherever hosted, can then use the exported connection_string to access and interact with the replicated MongoDB Atlas instances.

    For full details on the mongodbatlas.Cluster resource and its properties, you can refer to the MongoDB Atlas documentation on Pulumi.

    It is important to secure your AI services' access to MongoDB Atlas. Typically, this would involve setting up VPC peering or private endpoints if supported, and configuring network access control lists (ACLs) or security groups to restrict access only to the necessary sources.

    Remember to replace placeholder values (like 'your-atlas-project-id') with your actual MongoDB Atlas project Id and adjust other configurations according to your specific replication and AI service needs. Ensure that your MongoDB Atlas API keys (publicKey and privateKey) are set as secrets in your Pulumi configuration; this prevents sensitive information from being exposed.