Marketing data from multiple sources is uploaded to an Amazon S3 bucket. Data preparation jobs must run at regular intervals in parallel, with a few jobs that must run in a specific order later. The company wants to eliminate operational overhead for job error handling, retries, and state management. Which solution meets these requirements?

This solution is ideal because AWS Step Functions excels at orchestrating complex workflows, including parallel execution and sequential steps, while managing state, retries, and error handling automatically. This directly addresses the requirement to eliminate operational overhead for job error handling, retries, and state management. AWS Glue DataBrew is a visual data preparation tool that can process data from S3, making it suitable for the "data preparation jobs" requirement. Using Lambda functions directly for complex, scheduled, and ordered workflows becomes cumbersome for state management and error handling. Athena is for querying data, not for scheduled, ordered data preparation jobs. AWS Data Pipeline is an older service, and Step Functions offers more robust and flexible workflow orchestration capabilities.