1. Unified Data Repositories for LLMs with MongoDB Atlas Federated Database Instances

    Python

    Creating a unified data repository with MongoDB Atlas involves setting up federated database instances that allow seamless data integration and querying across various data sources. MongoDB Atlas is a fully-managed cloud database service that offers robust scalability and flexibility for handling data.

    Pulumi provides an easy-to-use infrastructure as code approach to set up and manage complex resources like MongoDB Atlas federated instances. Below I will explain the key resources from the MongoDB Atlas provider and how they are used to create a unified data repository.

    1. mongodbatlas.Cluster: This resource is used to create and manage a MongoDB cluster within Atlas. It supports various configurations such as the cluster size, backup settings, and the database version.
    2. mongodbatlas.FederatedSettingsIdentityProvider: This resource is needed when you want to manage federated identity providers. This can be used for setting up single sign-on (SSO) for unified access across your federated database instances.
    3. mongodbatlas.DataLake: The Data Lake resource is specifically designed for querying data across different sources within MongoDB Atlas. This allows you to integrate different data repositories, accessing them as if they were a single data source.
    4. mongodbatlas.ServerlessInstance: With serverless instances, you can run your applications without managing the infrastructure. This can be integrated into your unified data repository to handle requests and operations without the need for a dedicated server setup.
    5. mongodbatlas.GlobalClusterConfig: This can be essential if you need to configure a global cluster that enables you to place data closer to end-users for lower latency access.

    For simplicity, I'll demonstrate how to create a MongoDB cluster and a Data Lake, which will form the core of your unified data repository. The cluster will hold your operational data, and the Data Lake will enable you to query that data seamlessly across various sources.

    Here's a Pulumi Python program to set up a MongoDB Atlas cluster and configure a Data Lake for a unified data view:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Configure the MongoDB Atlas provider with required credentials. # The credentials can usually be set in your environment or via the Pulumi config file. # You would need to replace `<PROJECT-ID>` with your actual MongoDB Atlas project id. mongodbatlas_provider = mongodbatlas.Provider("mongodb-atlas-provider", project_id="<PROJECT-ID>") # Create a MongoDB Atlas cluster. # This sets up your primary data store where your operational database will reside. # Replace `<CLUSTER-NAME>` and other parameters with your desired configuration. cluster = mongodbatlas.Cluster("mongo-cluster", name="<CLUSTER-NAME>", projectId="<PROJECT-ID>", providerName="AWS", # Assuming we are deploying on AWS. providerRegionName="us-east-1", # Specify the region where you want your cluster. providerInstanceSizeName="M30", diskSizeGb=20, # Define the disk size for your cluster. opts=pulumi.ResourceOptions(provider=mongodbatlas_provider)) # Create a MongoDB Atlas Data Lake. # This Data Lake allows you to perform SQL-like queries across your MongoDB instances # and other integrated data sources. Replace `<DATA-LAKE-NAME>` with your desired name. data_lake = mongodbatlas.DataLake("mongo-data-lake", projectId="<PROJECT-ID>", name="<DATA-LAKE-NAME>", aws=mongodbatlas.DataLakeAwsArgs( external_id="<YOUR-AWS-EXTERNAL-ID>", # Optional, depending on your AWS configuration. iam_assumed_role_arn="<AWS-IAM-ROLE-ARN>", # The IAM role that Data Lake will assume. test_s3_bucket="<AWS-S3-BUCKET-FOR-TESTING>" # S3 bucket for testing Data Lake integration. ), data_process_region=mongodbatlas.DataLakeDataProcessRegionArgs( region="us-east-1", # The region where data processing should occur. cloud_provider="AWS"), # The cloud provider you're integrating with. opts=pulumi.ResourceOptions(provider=mongodbatlas_provider)) # Export the cluster's connection string for future use, such as for applications that will connect to the cluster. pulumi.export("mongo_cluster_connection_string", cluster.connection_strings.apply(lambda cs: cs.standard)) # Similarly, export the Data Lake's endpoint for integration with other systems or applications. pulumi.export("mongo_data_lake_endpoint", data_lake.endpoint)

    In this program:

    • We initialize a Provider resource which handles the connection and execution against the MongoDB Atlas API with the given project id and credentials.
    • A MongoDB cluster is created with its configuration details such as instance size and disk size.
    • A Data Lake associated with the project is set up. Note the use of DataLakeAwsArgs which specifies details related to AWS, assuming that is the cloud provider in use.

    Make sure to replace placeholder values such as <PROJECT-ID>, <YOUR-AWS-EXTERNAL-ID>, <AWS-IAM-ROLE-ARN>, <AWS-S3-BUCKET-FOR-TESTING>, and <CLUSTER-NAME> with actual values that apply to your environment.

    Remember to configure your Pulumi project correctly with the necessary access rights and API tokens to interact with MongoDB Atlas. This is often done via environment variables or the Pulumi configuration system.


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