1. Consolidated Analytics Platform with MongoDB Atlas Federated Database Instances

    Python

    To create a Consolidated Analytics Platform using MongoDB Atlas Federation, we'll leverage Pulumi to provision and configure the necessary MongoDB Atlas resources. MongoDB Atlas is a fully-managed cloud database developed by the same people that build MongoDB. The Atlas platform is available on AWS, Azure, and Google Cloud, providing a centralized interface to handle various database instances, including federated database instances that can allow for distributed data analysis.

    Here's how you would use Pulumi with the pulumi_mongodbatlas plugin to create federated database instances on MongoDB Atlas:

    1. MongoDB Atlas Project: We need a Project to contain our MongoDB clusters.
    2. Atlas Cluster: This is where our MongoDB database will reside. We'll define the desired cluster size, region, and specific configurations necessary for our analytics platform.
    3. Federated Database Instance: An Atlas Cluster can be part of a federated database. Pulumi creates instances that are joined into a federated database so that you can query data across different Atlas clusters.
    4. Data Lake: MongoDB Atlas Data Lakes allow querying data from multiple clusters and different sources using the MongoDB Query Language (MQL). Pulumi can create an Atlas Data Lake to serve as part of the analytics platform.

    Note: The actual implementation of federated database instances involves specifying configurations like globalClusterConfig and federatedSettingsIdentityProvider, which allow you to manage federated instances globally and integrate identity providers. The process also involves handling user authentication, networking, and database schema setups, which might require further setup and are not covered in this basic provision.

    Below is a Pulumi program in Python that provides a starting point for provisioning these resources. This program assumes you have already set up Pulumi, have the pulumi_mongodbatlas plugin installed, and have the appropriate MongoDB Atlas credentials configured in Pulumi.

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Create a MongoDB Atlas Project project = mongodbatlas.Project("my-project", org_id="<YOUR_ORG_ID>") # Create an Atlas Cluster within the project cluster = mongodbatlas.Cluster("my-cluster", project_id=project.id, name="my-federated-cluster", disk_size_gb=10, provider_name="AWS", provider_region_name="US_EAST_1", mongo_db_major_version="4.4", # Specify other options based on your use-case ) # Create a Data Lake that federates data across clusters for queries data_lake = mongodbatlas.DataLake("my-datalake", project_id=project.id, name="my-analytics-datalake", data_process_region={ "cloud_provider": "AWS", "region": "US_EAST_1", }, storage_databases=[{ "name": "analytics", "collections": [{ "name": "coll", "dataSources": [{ "store_name": "primary", "cluster_name": cluster.name.apply(lambda name: f"{name}"), "project_id": cluster.project_id, }] }], }], ) # Export the important attributes pulumi.export("project_id", project.id) pulumi.export("cluster_name", cluster.name) pulumi.export("datalake_name", data_lake.name)

    In this program:

    • project_id is your MongoDB Atlas Organization ID.
    • cluster represents the MongoDB cluster configured to accept federated queries.
    • data_lake is the Atlas Data Lake configured to query across federated database instances in the cluster.

    It's important to replace <YOUR_ORG_ID> with the actual MongoDB Atlas organization ID which can be found in your MongoDB Atlas dashboard.

    This program results in the creation of MongoDB Atlas resources that form the backbone of an analytics platform. You can build on this foundation by configuring analytics tools, integrating data sources, and setting up access controls as needed.


GPT4o|Modeexplainbalancedcode