1. Real-time Data Processing for AI with AWS Lambda and S3


    Real-time data processing is a critical component of AI-driven applications, such as those performing analytics on streaming data or requiring immediate action based on the incoming data streams. AWS provides a robust stack of services that can help achieve real-time data processing, with AWS Lambda for compute and S3 for storage. In the context of Pulumi, you can use infrastructure-as-code to define and deploy these services programmatically.

    Below, I will guide you through setting up a simple real-time data processing pipeline using AWS Lambda and S3 with Pulumi in Python. The basic idea is that files uploaded to an S3 bucket will trigger a Lambda function to process the data immediately.

    We will create the following resources:

    1. An S3 bucket: This is the storage where your files or streaming data will reside.
    2. A Lambda function: This serverless compute service will process the data in real-time. It will be triggered whenever a new file is uploaded to the S3 bucket.
    3. A Lambda permission: This grants the S3 service the necessary permission to invoke the Lambda function in response to events such as file uploads.

    Let's look at how you can define this setup:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store files s3_bucket = aws.s3.Bucket("data_bucket") # Create an IAM role and policy to allow Lambda to access other AWS services # The assume role policy allows Lambda services to assume this role lambda_role = aws.iam.Role("lambda_role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""" ) # Attach the AWS Lambda Basic Execution Role policy to the IAM role lambda_exec_policy_attachment = aws.iam.RolePolicyAttachment("lambda_exec_policy_attachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole" ) # Create the Lambda function lambda_function = aws.lambda_.Function("data_processor_function", runtime="python3.8", # Choose the runtime environment for Lambda code=pulumi.FileArchive("./function"), # Specify the path to the Lambda function code handler="handler.handler", # The function entrypoint in our code role=lambda_role.arn, timeout=300, # Maximum time that the function can run (in seconds) memory_size=128, # Set the memory allocated for the Lambda function # This is the environment variable that we can access inside our Lambda function environment=aws.lambda_.FunctionEnvironmentArgs( variables={ "BUCKET_NAME": s3_bucket.bucket # Passes our S3 bucket name to the Lambda environment } ), opts=pulumi.ResourceOptions(depends_on=[lambda_exec_policy_attachment]) ) # Grant the S3 bucket permission to invoke the Lambda function lambda_permission = aws.lambda_.Permission("lambda_permission", action="lambda:InvokeFunction", function=lambda_function.name, principal="s3.amazonaws.com", source_arn=s3_bucket.arn ) # Event to link S3 object creation to Lambda function execution s3_bucket_notification = aws.s3.BucketNotification("s3_bucket_notification", bucket=s3_bucket.id, lambda_functions=[aws.s3.BucketNotificationLambdaFunctionArgs( lambda_function_arn=lambda_function.arn, events=["s3:ObjectCreated:*"], filter_prefix="input/", # Only trigger for files uploaded to 'input/' directory filter_suffix=".json" # Only trigger for files with '.json' extension )], opts=pulumi.ResourceOptions(depends_on=[lambda_permission]) ) # Export the name of the bucket and the Lambda function ARN pulumi.export("bucket_name", s3_bucket.bucket) pulumi.export("lambda_function_arn", lambda_function.arn)

    Here’s a step-by-step walkthrough of the code:

    1. S3 Bucket: We create an S3 bucket using aws.s3.Bucket. This is where the data files will be stored.

    2. IAM Role and Policy: Before creating the Lambda function, we establish an IAM role (aws.iam.Role) with the necessary policies allowing it to execute and access other AWS services.

    3. Lambda Function: We define the Lambda function using aws.lambda_.Function, specifying the runtime, code location, handler, associated role, and other configurations.

    4. Lambda Permission: The aws.lambda_.Permission resource is then created to allow S3 services to invoke the Lambda function upon certain events, like the creation of an object.

    5. Bucket Notification: Finally, aws.s3.BucketNotification links the S3 bucket events to the Lambda function. We specify that the function should be invoked whenever a new .json file is uploaded to the input/ path of the bucket.

    6. Pulumi Exports: We export the bucket name and Lambda function ARN for easy reference.

    To execute this Pulumi program, you will need a directory with your Lambda code. For instance, if your Lambda function code is in function/handler.py, ensure it has the logic needed to process your data.

    Please remember to replace "./function" with the actual file path of your Lambda code when deploying this stack. The Lambda function's directory should be placed at the same level as your Pulumi.yaml file or you should adjust the path accordingly.