1. Real-time Stream Processing for AI with AWS Data Pipeline.


    To set up real-time stream processing for AI with AWS Data Pipeline, you're going to create a pipeline that includes a data node to capture and store data, a compute node to process the data, and a data store to output the results. AWS Data Pipeline allows you to automate the movement and transformation of data. With the real-time processing requirement and AI focus, you might also consider using services like AWS Kinesis or AWS Lambda to process streaming data, depending on your AI workload.

    Below is a program written in Python using Pulumi to set up a basic AWS Data Pipeline. This pipeline uses AWS Lambda to process data in real-time and AWS S3 as a data node to collect processed results. Keep in mind, this example is a starting point, and you will need to customize the Lambda function as per your AI use-case:

    import pulumi import pulumi_aws as aws # Create an IAM role that AWS Data Pipeline can assume data_pipeline_role = aws.iam.Role("data_pipeline_role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "datapipeline.amazonaws.com" }, "Action": "sts:AssumeRole" } ] }""") # Create an S3 bucket to store logs and outputs s3_bucket = aws.s3.Bucket("s3_bucket") # Create an AWS Lambda function for real-time data processing # NOTE: `handler` and `runtime` will vary depending on your specific use case and code. lambda_function = aws.lambda_.Function("lambda_function", code=pulumi.FileArchive("./path_to_your_lambda_code.zip"), # Replace with path to your Lambda function code handler="index.handler", # Replace with your handler information role=data_pipeline_role.arn, runtime="python3.8") # Choose runtime as appropriate for your Lambda code # Define the AWS Data Pipeline using the created resources data_pipeline = aws.datapipeline.Pipeline("data_pipeline", description="A pipeline for real-time stream processing for AI", tags={ "Name": "data_pipeline" }) # Define the pipeline objects # This is a simplified example. You would need to define your source and destination properly. # The example below assumes that you will trigger Lambda and store results in S3. pipeline_definition = aws.datapipeline.PipelineDefinition("pipeline_definition", pipeline_id=data_pipeline.id, parameter_values=[ {"id": "myInputDataNode", "stringValue": "s3_input_data_location"}, # Example input data location {"id": "myOutputDataNode", "stringValue": s3_bucket.bucket.apply(lambda b: f"s3://{b}")}, # S3 bucket URL {"id": "myLambdaProcessor", "stringValue": lambda_function.arn}, # ARN of the Lambda function ], pipeline_objects=[ {"id": "Default", "name": "Default"}, {"id": "Schedule", "name": "Schedule", "fields": [ {"key": "type", "stringValue": "Schedule"}, {"key": "period", "stringValue": "1 Hour"}, {"key": "startAt", "stringValue": "FIRST_ACTIVATION_DATE_TIME"}, ]}, { "id": "myInputDataNode", "name": "MyInputDataNode", "fields": [ {"key": "type", "stringValue": "S3DataNode"}, {"key": "directoryPath", "stringValue": "${myInputDataNode}"}, # Bind the parameter values ], }, { "id": "myLambdaProcessor", "name": "MyLambdaProcessor", "fields": [ {"key": "type", "stringValue": "Lambda"}, {"key": "script", "stringValue": "lambda_function"}, # Reference to your Lambda function {"key": "resultS3Location", "stringValue": "${myOutputDataNode}"}, # S3 output {"key": "actionOnResourceFailure", "stringValue": "retryAll"}, ], }, ]) # Export the URLs to access the Lambda function and the S3 bucket pulumi.export("lambda_function_url", lambda_function.arn) pulumi.export("s3_bucket_url", s3_bucket.bucket.apply(lambda b: f"s3://{b}"))

    In this Pulumi program:

    1. IAM Role: An AWS IAM role data_pipeline_role is created for the AWS Data Pipeline service to assume.

    2. S3 Bucket: An S3 bucket s3_bucket is created to store logs and output results.

    3. AWS Lambda Function: An AWS Lambda function lambda_function is set up to process the real-time data streaming. You need to replace ./path_to_your_lambda_code.zip with the path to your Lambda function's deployment package, and index.handler with the handler in your Lambda code. Choose an appropriate runtime for your code (e.g., Python, Node.js).

    4. AWS Data Pipeline: An AWS Data Pipeline data_pipeline is defined to manage the movement and transformation of data based on the parameters and objects defined further in the code.

    5. Pipeline Definition: The pipeline_definition defines the actual workflow of the AWS Data Pipeline. Replace s3_input_data_location with the S3 location where your data is being ingested. Modify the input data node, schedule, and Lambda processor as per your data processing requirements.

    6. Export: At the end, the program exports the Lambda function ARN and the S3 bucket URL so that you know where your resources are and can access them outside of Pulumi.

    To run your Pulumi program:

    1. Install Pulumi and set up the AWS provider.
    2. Place this Python code into a __main__.py file.
    3. Run pulumi up to deploy your infrastructure.

    Remember, you will need to provide your actual AI processing code for the Lambda function and have the appropriate permissions configured for all services involved.