1. Decoupling Data Ingestion and Processing for AI Pipelines


    Decoupling data ingestion and processing is a common architectural pattern, especially for AI and machine learning workflows. It involves separating the data collection phase from the data processing phase. This separation enhances scalability, manageability, and fault tolerance. Cloud services offer various tools for this purpose, and with Pulumi, you can define these resources as code, making it easier to deploy and replicate environments.

    In this guide, I will demonstrate how to create a simple decoupled data ingestion and processing setup, using AWS S3 for data storage, AWS Lambda for data processing, and Amazon EventBridge for triggering the Lambda function when new data arrives.

    How it Works:

    1. Data Ingestion: Data is deposited into an S3 bucket. This could be structured data, unstructured data, or any form that is ingestible by an S3 bucket.
    2. Event Triggering: An EventBridge rule is configured to trigger upon the arrival of new data in the S3 bucket.
    3. Data Processing: An AWS Lambda function is triggered by the EventBridge rule, processes the data, and possibly stores the results in another location or database for further use, such as training a machine learning model.

    Here's how we can write a Pulumi program in Python to implement this architecture.

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store the data data_bucket = aws.s3.Bucket("dataBucket") # Create an IAM role that grants the necessary permissions to the Lambda function lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""") # Attach policies to the Lambda role to allow it to access the S3 bucket aws.iam.RolePolicyAttachment("lambdaS3Access", role=lambda_role, policy_arn=aws.iam.ManagedPolicy.AMAZON_S3_FULL_ACCESS.value ) # The Lambda function that processes the data lambda_function = aws.lambda_.Function("dataProcessor", role=lambda_role.arn, # Use your Lambda function code here (this would ideally be packaged as a .zip file) handler="index.handler", # the function within your code to invoke runtime=aws.lambda_.Runtime.PYTHON3_8, # specify the runtime environment code=pulumi.FileArchive("./lambda.zip") # path to your zipped code ) # Create an EventBridge rule to trigger on put events in the data_bucket s3_event_rule = aws.cloudwatch.EventRule("s3EventRule", event_pattern=f"""{{ "source": ["aws.s3"], "detail-type": ["AWS API Call via CloudTrail"], "detail": {{ "eventSource": ["s3.amazonaws.com"], "eventName": ["PutObject"], "requestParameters": {{ "bucketName": ["{data_bucket.id}"] }} }} }}""" ) # Attach the rule to the Lambda function aws.cloudwatch.EventTarget("s3EventTarget", rule=s3_event_rule.name, arn=lambda_function.arn ) # Create necessary permissions for EventBridge to invoke the Lambda function lambda_permission = aws.lambda_.Permission("lambdaPermission", action="lambda:InvokeFunction", function=lambda_function.name, principal="events.amazonaws.com", source_arn=s3_event_rule.arn ) # Export the S3 bucket name pulumi.export('bucket_name', data_bucket.id)


    • S3 Bucket (data_bucket): This is where your data will be uploaded. When new files are added to the bucket, it will trigger the EventBridge rule.
    • IAM Role (lambda_role): AWS Lambda requires permissions to execute your function, including access to any AWS services the function interfaces with. The role defines these permissions.
    • Lambda Function (lambda_function): This is the processing unit. The provided code for Lambda should include logic to handle and process the incoming data.
    • EventBridge Rule (s3_event_rule): The rule watches for PutObject events on the S3 bucket, a common signifier that new data has been uploaded and is ready for processing.
    • Event Target (s3EventTarget): Connects the EventBridge rule to our Lambda function, so when the rule condition is met, EventBridge knows to trigger the Lambda function.
    • Lambda Permission (lambda_permission): Grants EventBridge permission to invoke the Lambda function when the rule is triggered.

    Remember to upload your Lambda function code as a zipped archive at the specified path in the program. Modify the handler attribute to point to the proper function handler within your code.

    Ensure you have Pulumi CLI installed and configured with AWS credentials to deploy this stack. To deploy the resources defined in this program, run pulumi up in the directory where this file is located.