AWS Glue workflows are specifically designed to orchestrate extract, transform, and load (ETL) activities. They provide a fully managed, serverless environment to create, visualize, and run complex data processing pipelines involving AWS Glue jobs, crawlers, and triggers. For a company already using AWS Glue and Amazon EMR, Glue workflows offer the most direct and native method for automation. This approach minimizes operational overhead as it is a built-in feature of AWS Glue, requiring no separate infrastructure management, unlike other options. It directly addresses the need for automated orchestration with minimal manual effort for the existing ETL processes.

1. AWS Glue Developer Guide

"Building a complex ETL workflow with AWS Glue": "You can use the AWS Glue console to create a complex extract

transform

and load (ETL) workflow with interdependent jobs

crawlers

and triggers. Each workflow manages the execution and monitoring of all its components... This provides a simple

graph-based way to author and visualize your data processing pipeline." This highlights its purpose-built nature for ETL orchestration.

2. AWS Glue Developer Guide

"Overview of AWS Glue": "AWS Glue is a fully managed ETL (extract

transform

and load) service... It is serverless

so there is no infrastructure to set up or manage." This confirms the low operational overhead of the service and its integrated features like workflows.

3. AWS Step Functions Developer Guide

"What is AWS Step Functions?": "AWS Step Functions is a serverless orchestration service that lets you integrate with AWS Lambda functions and other AWS services..." While capable

its general-purpose nature makes it less specific than Glue Workflows for this ETL-focused scenario.

4. Amazon MWAA User Guide

"What is Amazon MWAA?": "Amazon Managed Workflows for Apache Airflow (MWAA) is a managed orchestration service for Apache Airflow... You can choose a range of available compute resources to execute your workflows." The need to choose and manage compute resources indicates higher operational overhead compared to a fully serverless option.