1. Real-time Data Ingestion for AI Streaming with AWS API Gateway


    To set up real-time data ingestion for AI streaming using AWS API Gateway, you would typically need to create an API Gateway, configure it to handle incoming data (the "stream"), and set up a backend service that processes this data in real-time — often with a serverless service like AWS Lambda.

    We'll be using Pulumi to provision these AWS resources. Here’s an overview of the main components we’ll create and why:

    1. API Gateway: This serves as the entry point for the data stream. Clients send data to an API endpoint, and the API Gateway processes it according to the configured routes and methods.

    2. Lambda Function: AWS Lambda will handle the real-time processing of the incoming data stream. Once the data hits the API Gateway, it will be forwarded to a Lambda function for processing.

    3. IAM Role and Policy: A role for AWS Lambda will be created with the necessary permissions that the Lambda function will assume when being executed.

    4. DynamoDB Table (optional): If you need to store the ingested data, DynamoDB can provide a managed NoSQL database solution. It's capable of handling large amounts of data with low latency.

    Below is a Pulumi program in Python that sets up an API Gateway linked to a Lambda function. The Lambda function will simply log the data for demonstration purposes, but in a real scenario, it'd be where you apply your AI logic.

    import pulumi import pulumi_aws as aws # Create an IAM role that allows the Lambda function to run 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 the AWS Lambda Basic Execution Role to the Lambda function role_policy_attachment = aws.iam.RolePolicyAttachment("lambdaRoleAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole") # Create the Lambda function that will receive the incoming data lambda_function = aws.lambda_.Function("myLambdaFunction", code=pulumi.FileArchive("./function.zip"), # REPLACE with path to your Lambda function deployment package role=lambda_role.arn, handler="handler.main", # REPLACE with your function entry point runtime="python3.8") # REPLACE with your Lambda function runtime # Create the API Gateway for the data stream api = aws.apigatewayv2.Api("myApi", protocol_type="HTTP", route_key="POST /data", # Replace with your desired route target=lambda_function.invoke_arn) # Create a permission to allow the API Gateway to invoke the Lambda function lambda_permission = aws.lambda_.Permission("lambdaPermission", action="lambda:InvokeFunction", function=lambda_function.name, principal="apigateway.amazonaws.com", source_arn=api.execution_arn.apply(lambda arn: arn + "/*/*")) # Export the API endpoint for easy access pulumi.export("api_endpoint", api.api_endpoint)

    This program does the following:

    • Sets up an IAM role and policy to give the necessary permissions for the Lambda function to execute.
    • Defines a Lambda function with the code you will provide. You need to zip your Lambda code into function.zip and specify the handler and runtime you're using.
    • Creates an API Gateway (api) configured for an HTTP protocol which routes POST requests to /data to trigger the Lambda function.
    • Grants permission (lambda_permission) for the API Gateway to invoke the Lambda function.
    • Finally, the endpoint URL of the API Gateway (api_endpoint) is exported so you can easily access it to send data to your ingestion system.

    Replace the placeholders with your actual Lambda function code, handler, and runtime. The Lambda function code should be in a directory, zipped, and its path should replace ./function.zip. The handler is the entry point of your Lambda function. For a file named handler.py with a function main, the handler is handler.main. Make sure to use the correct runtime that matches your Lambda function's programming language and version.

    After deploying this Pulumi stack, you will get an endpoint that can ingest data. When data is POSTed to this endpoint, it will trigger the Lambda function where your real-time processing logic can be applied.

    Do note that the program is a starting point and doesn't include setting up a storage solution like DynamoDB or error handling which would be essential for a production environment.