1. Real-time Analytics for AI with Azure Cosmos DB


    Real-time analytics for AI applications can be demanding in terms of database performance, particularly when it comes to throughput and latency. Azure Cosmos DB is a fully managed, globally distributed database service designed to provide low-latency, high-throughput, and tunable consistency with multiple data models and popular APIs for accessing data. This makes it a good choice for AI applications that require real-time analytics.

    To set up a real-time analytics platform for AI, we need to create an Azure Cosmos DB account and a database within it. For purposes of analytics, you might be interested in the SQL (Core) API, which enables rich querying over schema-less JSON data, or the Gremlin API if you're dealing with graph-based data. For AI applications that need to process large amounts of data quickly, we can use features like autoscaling to automatically adjust throughput based on the workload, and enable multi-region writes for higher availability and performance.

    Below is a program in Python using Pulumi to create an Azure Cosmos DB account with a SQL (Core) API database suitable for real-time analytics:

    import pulumi import pulumi_azure_native as azure_native # Create a resource group for the Cosmos DB account resource_group = azure_native.resources.ResourceGroup('my-resource-group') # Create a Cosmos DB account with the SQL API for real-time analytics workloads cosmosdb_account = azure_native.documentdb.DatabaseAccount('my-cosmosdb-account', resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", # The offer type for the Cosmos DB account (Standard or Basic). locations=[{ 'locationName': resource_group.location, # Primary location for the Cosmos DB account 'failoverPriority': 0, }], consistency_policy={ 'defaultConsistencyLevel': 'Session', # Session consistency offers predictable guarantees for reads }, enable_automatic_failover=True, # Enable automatic failover to the failover location enable_multiple_write_locations=True, # Enable multiple write locations for global distribution ) # Create a SQL database within the Cosmos DB account for storing JSON documents cosmos_sql_database = azure_native.documentdb.SqlResourceSqlDatabase('my-cosmos-sql-database', resource_group_name=resource_group.name, account_name=cosmosdb_account.name, resource={ 'id': 'my-sql-db' # User-defined unique database id }, options={ 'throughput': 400 # Set throughput for the database (measured in Request Units per second) } ) # Export the Cosmos DB account endpoint and primary key to use with the application primary_master_key = cosmosdb_account.primary_master_key.apply(lambda key: key) account_endpoint = cosmosdb_account.document_endpoint.apply(lambda endpoint: endpoint) pulumi.export('account_endpoint', account_endpoint) pulumi.export('primary_master_key', primary_master_key)

    In the code above:

    • We start by defining a resource group to contain our Cosmos DB resources.
    • We then create an Azure Cosmos DB account, enabling automatic failover and multiple write locations for high availability and global distribution.
    • A SQL (Core) API database is created in the account, which is suitable for storing and querying JSON documents in real-time.
    • Finally, we export the Cosmos DB account endpoint and primary master key, which can be used by your application to interact with the database.

    General Notes:

    • The Azure Cosmos DB account is set up with Session consistency level to provide a balance between read consistency and throughput.
    • The SQL database is provisioned with a fixed amount of throughput (400 RUs). This can be scaled up or down based on your workload needs. Consider using autoscale settings if your workload is variable.
    • Always manage your keys securely outside of your source code when running in production environments.

    This program sets up the infrastructure for a real-time AI analytics platform on Azure. The use of Cosmos DB here is for its globally distributed capabilities and low-latency reads and writes, which are essential for AI workloads.