1. Event Sourcing for AI Systems with Azure Table Storage


    Event sourcing is an architectural pattern where state changes are logged as a sequence of immutable events. When implementing event sourcing for AI systems on Azure, you'll typically use Azure Table Storage to keep the event logs due to its schema-less design, which is suitable for the dynamic nature of events.

    In this case, we will create a Table in Azure Table Storage, which will act as our storage for the event logs. Here's what we're going to do:

    1. Set up an Azure Resource Group, which is a container that holds related resources for an Azure solution.
    2. Create an Azure Storage Account, which provides the base for storing many types of data.
    3. Create a Table within the Storage Account to store our event logs.

    Here's the Pulumi Python program that sets up these resources:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('ai-event-sourcing-rg') # Create an Azure Storage Account storage_account = azure_native.storage.StorageAccount('aieventsourcingstorage', resource_group_name=resource_group.name, sku=azure_native.storage.SkuArgs( name=azure_native.storage.SkuName.STANDARD_LRS, ), kind=azure_native.storage.Kind.STORAGE_V2, location=resource_group.location) # Create a Table in the Azure Storage Account for storing events table = azure_native.storage.Table('eventsTable', resource_group_name=resource_group.name, account_name=storage_account.name) # Export the connection string for the storage account which can be used to access the Table primary_connection_string = pulumi.Output.all(resource_group.name, storage_account.name).apply( lambda args: azure_native.storage.list_storage_account_keys_output.ListStorageAccountKeysOutput(args[0], args[1]).apply( lambda account_keys: f"DefaultEndpointsProtocol=https;AccountName={args[1]};AccountKey={account_keys.keys[0].value};EndpointSuffix=core.windows.net")) pulumi.export('primary_storage_connection_string', primary_connection_string) # Export the name of the table which can be used in applications to reference the event storage pulumi.export('table_name', table.name)

    Let's go over the components we used in the program:

    • ResourceGroup: This acts as a logical container for your Azure services. Every service you deploy is associated with a ResourceGroup. It's a way to manage resources collectively.

    • StorageAccount: This is a Microsoft Azure service that provides highly available and scalable cloud storage. Here, it's configured to use a general-purpose v2 account (which supports the latest Azure Storage features) with a "Locally Redundant Storage" (LRS) replication strategy.

    • Table: This is the Azure Table Storage resource. It's within the Storage Account we created and will be used to store our event logs.

    At the end of the program, there are export statements:

    • primary_storage_connection_string: This is the connection string for the storage account. It's necessary for accessing the storage from applications.

    • table_name: This is the name of the table we're using to store events, which applications will use to direct read and write operations.

    Putting it all together, this Pulumi program will set up the essential Azure resources for event sourcing in Azure Table Storage and produce outputs that can be used to connect to the storage from your AI applications.