1. AI Data Ingestion Endpoints with AWS API Gateway


    To create AI Data Ingestion Endpoints using AWS API Gateway, you'll want to define resources within Pulumi that represent your API Gateway configuration, as well as any other AWS services that you're integrating with (such as AWS Lambda for processing the data).

    Below, you'll find a Pulumi program that sets up an API Gateway endpoint, which is configured to trigger a Lambda function upon HTTP POST requests. The Lambda function could be where you implement your data ingestion logic.

    I'll walk you through the Pulumi resources we're going to use:

    • aws.lambda.Function: This resource is used to create an AWS Lambda function. The Lambda function will contain the logic necessary to process the incoming data. You can think of it as the back-end processing unit of our ingestion service.
    • aws.apigateway.RestApi: This represents an API Gateway REST API. It's basically our entry point for the ingestion service.
    • aws.apigateway.Resource: A part of the API that defines a route.
    • aws.apigateway.Method: HTTP methods (e.g., 'POST') that are allowed for a given resource.
    • aws.apigateway.Integration: This glues together the API Gateway resource and the Lambda function. It's what tells API Gateway to trigger the Lambda function when the associated method is invoked.
    • aws.iam.Role and aws.iam.RolePolicyAttachment: These IAM resources grant the necessary permissions for the API Gateway to invoke the Lambda function.

    Let's put these together in a Pulumi program:

    import pulumi import pulumi_aws as aws # Create an IAM role for 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 a policy to the IAM 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 lambda_function = aws.lambda_.Function("dataIngestionFunction", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./app") # Your Lambda Function code inside `app` folder }), role=lambda_role.arn, handler="index.handler", # Assuming the ingest handler function is defined as `handler` in `index.js` runtime="python3.8") # Or any other runtime you're using # Create the REST API api = aws.apigateway.RestApi("dataIngestionApi", description="API for data ingestion") # Create a resource representing our ingestion endpoint resource = aws.apigateway.Resource("ingestionResource", rest_api=api.id, parent_id=api.root_resource_id, path_part="ingest") # Endpoint will be /ingest # Attach a POST method to the ingestion resource method = aws.apigateway.Method("ingestionMethod", rest_api=api.id, resource_id=resource.id, http_method="POST", authorization="NONE") # Integrate the POST method with the Lambda function integration = aws.apigateway.Integration("lambdaIntegration", rest_api=api.id, resource_id=resource.id, http_method=method.http_method, integration_http_method="POST", type="AWS_PROXY", uri=lambda_function.invoke_arn) # Deploy the API deployment = aws.apigateway.Deployment("apiDeployment", rest_api=api.id, # This empty depends_on array can be removed if you add actual dependencies depends_on=[], # To trigger a new deployment on changes to the API, you can set the `triggers` argument triggers={ "redeployment": pulumi.asset.FileArchive("./app").hash(), }) # Create a stage which points to our deployment stage = aws.apigateway.Stage("apiStage", rest_api=api.id, deployment=deployment.id, stage_name="v1") # Export the URL of the API endpoint pulumi.export("ingestion_endpoint", pulumi.Output.concat("https://", api.id, ".execute-api.", aws.config.region, ".amazonaws.com/", stage.stage_name, "/ingest"))

    This program will result in an API Gateway endpoint at a URL similar to https://<api-id>.execute-api.<region>.amazonaws.com/v1/ingest where HTTP POST requests can be sent.

    The above Pulumi code outlines the infrastructure as code needed to set up the AWS resources for your data ingestion endpoints. You'll need to provide the backend code for your Lambda function under the ./app directory, which contains the logic for your data ingestion.

    Remember that you need the AWS Pulumi provider set up and AWS credentials configured to run this Pulumi program successfully.