Apache Airflow is a workflow orchestration platform designed for scheduling and monitoring DAGs of tasks, while Apache Flink is a stream processing engine built for real-time data processing with complex event handling. Airflow excels at batch job orchestration; Flink dominates continuous streaming analytics.
Apache Airflow vs Apache Flink — Side-by-side comparison on A Versus B
Choose Apache Airflow if you need reliable batch job orchestration, complex DAG scheduling, and easier team onboarding with Python-based workflows. Choose Apache Flink if you require true real-time streaming analytics, complex event processing, and can invest in infrastructure and specialized expertise.
Open-source distributed workflow orchestration platform with DAG-based task scheduling and advanced monitoring.
Distributed stream processing engine for real-time analytics with advanced state management and exactly-once semantics
Apache Airflow vs Apache Flink — Comparison Data
Structured comparison data for Apache Airflow vs Apache Flink with 165 attributes.
Estimated Learning Curve (Hours to Productivity)
Setup Complexity (Configuration Files Required)
Enterprise Adoption (Fortune 500 Users Reported)
Community Repository Stars (as of Feb 2025)
Time to First Correct Result (learning curve)
Apache Airflow vs Apache Flink — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Apache Airflow vs Apache Flink.
Airflow can trigger streaming jobs but is not designed for continuous streaming processing. It's best used to orchestrate streaming job submissions and monitoring. For true streaming analytics, Flink is the proper choice.
Can Flink replace Airflow for batch workflows?
Yes, Flink has a unified batch and stream API and can run batch jobs. However, Flink's operational overhead and cost make it suboptimal for simple batch ETL. Airflow is better suited for batch orchestration unless you have streaming + batch unified requirements.
What's the cost difference between Airflow and Flink?
Airflow typically costs 40-60% less to operate because it's task-triggered and doesn't require always-on clusters. Flink requires continuous resource allocation for JobManager and TaskManagers, resulting in 2-3x higher infrastructure costs for equivalent throughput.
Which one should I choose for a real-time recommendation engine?
Apache Flink is the clear choice. It provides sub-millisecond latency, advanced state management for user profiles, and exactly-once processing semantics essential for recommendations. Airflow's latency (60-300ms minimum) is unsuitable for real-time personalization.
Yes, both support Kubernetes. Airflow runs lighter-weight with the KubernetesPodOperator. Flink integrates deeper with Kubernetes via the native Flink Kubernetes operator for cluster management, session management, and automatic scaling, though it's more operationally complex.