Which solution will meet these requirements with the MOST operational efficiency?
Use the AWS Step Functions Map state in Inline mode to process the data in parallel.
Use the AWS Step Functions Map state in Distributed mode to process the data in parallel.
Use AWS Glue to process the data in parallel.
Use several AWS Lambda functions to process the data in parallel.
Explanations:
Inline mode in AWS Step Functions Map state is designed for limited concurrency, and it may not handle thousands of items efficiently for large-scale, parallel processing.
Distributed mode in AWS Step Functions Map state is optimized for high concurrency and can handle thousands of items in parallel, making it ideal for large-scale, on-demand parallel processing of data stored in S3.
AWS Glue is a serverless data processing service, but it is primarily used for ETL tasks and is not the most efficient solution for on-demand, large-scale, parallel processing of various data types such as media files or logs.
Although AWS Lambda can process data in parallel, managing a large number of Lambda functions manually or with custom logic is less operationally efficient than using Step Functions Distributed mode for thousands of items.
From what I’ve seen, the answer is:
Use the AWS Step Functions Map state in Distributed mode to process the data in parallel.
From my point of view, the answer is:
Use the AWS Step Functions Map state in Distributed mode to process the data in parallel.