1. Global Distribution of AI Training Data with Azure Cosmos DB


    To globally distribute AI training data using Azure Cosmos DB, we'll create Azure Cosmos DB resources that enable multi-region writes and provide a globally distributed database system. This will ensure that your AI training data is replicated across multiple Azure regions, allowing for low-latency access to data regardless of where the requests originate.

    Here's a step-by-step guide on what we are going to create:

    1. Azure Cosmos DB Account: This is the Azure Cosmos DB service instance where databases, containers, and data are managed. We'll set up a multi-region account to ensure global distribution.

    2. Azure Cosmos DB SQL Database: A logical container for data in Azure Cosmos DB. We'll create a SQL-based database as it provides a rich set of features to manage and query JSON data.

    3. Azure Cosmos DB Containers: These are the structures within the SQL database that store JSON documents. We'll configure containers to organize the data in a meaningful way for the AI models to consume.

    4. Global Distribution Configuration: While setting up the above resources, we will configure the global distribution settings by specifying the regions that should participate in the distribution.

    Let's write the program that accomplishes this setup.

    import pulumi import pulumi_azure_native as azure_native # Define the resource group that will contain the Azure Cosmos DB resources. resource_group = azure_native.resources.ResourceGroup("ai_training_data") # Create the Azure Cosmos DB account with global distribution enabled. # We specify two locations here for multi-region writes, but you could add more as needed. cosmosdb_account = azure_native.documentdb.DatabaseAccount("CosmosDBAccount", resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", locations=[ azure_native.documentdb.InputLocationArgs( location_name="East US", failover_priority=0, ), azure_native.documentdb.InputLocationArgs( location_name="West Europe", failover_priority=1, ), ], enable_multiple_write_locations=True, # Enables multi-region writes. consistency_policy=azure_native.documentdb.ConsistencyPolicyArgs( default_consistency_level="Session", max_interval_in_seconds=5, max_staleness_prefix=100, ) ) # Create a SQL database in the Azure Cosmos DB account for AI training data. cosmosdb_sql_database = azure_native.documentdb.SqlResourceSqlDatabase("CosmosDbSqlDatabase", resource_group_name=resource_group.name, account_name=cosmosdb_account.name, resource=azure_native.documentdb.SqlDatabaseResourceArgs( id="AI_Training_Data", ), options=azure_native.documentdb.CreateUpdateOptionsArgs( throughput=400, # Define throughput (request units per second). ) ) # Create a container in the SQL database, specifically for training data. # Containers hold JSON documents and are the unit of scalability for both throughput and storage. training_data_container = azure_native.documentdb.SqlResourceSqlContainer("TrainingDataContainer", resource_group_name=resource_group.name, account_name=cosmosdb_account.name, database_name=cosmosdb_sql_database.name, resource=azure_native.documentdb.SqlContainerResourceArgs( id="TrainingDataset", ), options=azure_native.documentdb.CreateUpdateOptionsArgs( throughput=1000, # Throughputs are customizable per use-case. ) ) # Export the Cosmos DB account endpoint and primary key so that they can be used to access the database. primary_master_key = pulumi.Output.secret(cosmosdb_account.list_keys().primary_master_key) pulumi.export("endpoint", cosmosdb_account.document_endpoint) pulumi.export("primaryKey", primary_master_key)

    Let me explain further the Pulumi Python code we wrote above:

    • We start by creating an Azure Resource Group that serves as a logical container for our Azure Cosmos DB resources.

    • We then create an Azure Cosmos DB account configured for global distribution with two regions (East US and West Europe). You can add more regions depending on your needs for global replication.

    • Within this Cosmos DB account, we establish a SQL database, AI_Training_Data, which will organize our data.

    • We also create a container called TrainingDataset within the database. This container is where we store the JSON documents that contain our AI training data. The throughput setting can be adjusted according to the performance requirements of your training data access patterns.

    Lastly, we export the Cosmos DB endpoint and its primary master key. These will be used by your applications or services to interact with the Cosmos DB resources securely. Note that we mark the primary master key as a secret to protect it from exposure.

    By running this Pulumi program, you'll have set up a globally distributed Azure Cosmos DB ready to store and serve AI training data for your applications.