Apache Airflow vs AWS Glue

Choose Apache Airflow if:

We recommend Apache Airflow for data engineering teams that need a cloud-agnostic orchestration platform with maximum flexibility. Airflow is the stronger choice when your data pipelines span multiple clouds, on-premise systems, or diverse third-party services. Its Python-native DAG authoring, 45,100+ GitHub stars community, and extensive operator library make it the de-facto standard for complex workflow management. Choose Airflow when you have DevOps capacity to manage infrastructure, need fine-grained control over task dependencies and scheduling, or require orchestration that extends beyond ETL into ML pipelines, infrastructure management, and operational workflows.

Choose AWS Glue if:

We recommend AWS Glue for organizations that are heavily invested in the AWS ecosystem and want to minimize operational overhead. Glue eliminates infrastructure management entirely with its serverless architecture, automatic schema discovery, and built-in Spark processing engine. At $0.44 per DPU-hour, it offers predictable usage-based pricing without upfront commitments. Choose Glue when your data sources and destinations live primarily in AWS, when you need visual ETL authoring for less technical team members via DataBrew, or when you want built-in ML features like FindMatches deduplication and sensitive data detection without integrating external tools.