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
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
Primary Use Case. Processing Model. Latency. State Management. Learning Curve. Community Size. Cost at Scale
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.
Apache Airflow: 2.8M | Apache Flink: N/A
Apache Airflow: 8-16 hours | Apache Flink: N/A
Apache Airflow: 10+ via adapters | Apache Flink: N/A
Apache Airflow: Intermediate to Advanced | Apache Flink: N/A
Apache Airflow: 1,200+ | Apache Flink: N/A
Apache Airflow: E, L, T, testing, ML, monitoring (full stack) | Apache Flink: N/A
Apache Airflow: High (scheduler, web server, worker, database required) | Apache Flink: N/A
Apache Airflow: 120 minutes | Apache Flink: N/A
Apache Airflow: 50-70 lines | Apache Flink: N/A
Apache Airflow: 2,000+ operators | Apache Flink: 200+
Apache Airflow: 4+ (scheduler, worker, DB, broker) | Apache Flink: N/A
Apache Airflow: 35,000+ stars | Apache Flink: N/A
Apache Airflow: Self-hosted (varies) | Apache Flink: N/A
Apache Airflow: Community or third-party paid | Apache Flink: N/A
Apache Airflow: Complex (DAG operators) | Apache Flink: N/A
Apache Airflow: 22,000+ | Apache Flink: N/A
Apache Airflow: 9+ years (since 2015) | Apache Flink: N/A
Apache Airflow: Days (practical limit) | Apache Flink: N/A
Apache Airflow: Python (primary), Java/Go/C# (limited) | Apache Flink: N/A
Apache Airflow: 1,200+ official operators | Apache Flink: N/A
Apache Airflow: Manual retry + task checkpointing | Apache Flink: N/A
Apache Airflow: 5+ (scheduler, webserver, DB, executor, metadata) | Apache Flink: N/A
Apache Airflow: Yes (Astronomer, cloud providers) | Apache Flink: N/A
Apache Airflow: 9 years (2015) | Apache Flink: N/A
Apache Airflow: 1,000+ | Apache Flink: N/A
Apache Airflow: External tools required | Apache Flink: N/A
Apache Airflow: Minimal type hints, runtime validation | Apache Flink: N/A
Apache Airflow: Task-centric DAGs | Apache Flink: N/A
Estimated Learning Curve (Hours to Productivity)
Apache Airflow: 20-30 hours | Apache Flink: N/A
Apache Airflow: 150+ | Apache Flink: N/A
Apache Airflow: 1,800+ Providers | Apache Flink: N/A
Apache Airflow: 5-10 days | Apache Flink: N/A
Apache Airflow: 1-2 GB | Apache Flink: N/A
Apache Airflow: Yes (comprehensive) | Apache Flink: N/A
Apache Airflow: 500+ commits | Apache Flink: N/A
Apache Airflow: Yes (full support) | Apache Flink: N/A
Apache Airflow: Yes (PostgreSQL/MySQL) | Apache Flink: N/A
Apache Airflow: 10,000-3,600,000 ms (10 seconds to 1 hour typical) | Apache Flink: 1-100 ms (sub-second typical)
Apache Airflow: ~1,000-5,000 tasks/min | Apache Flink: 100,000-1,000,000 events/sec
Apache Airflow: 5-15 minutes (quick setup) | Apache Flink: 20-40 hours (learning + development)
Apache Airflow: Fully native (DAG definitions in pure Python) | Apache Flink: PyFlink added in v1.11 (2020); improved in v1.14+
Apache Airflow: At-least-once (with retries) | Apache Flink: Exactly-once (configurable per checkpoint)
Apache Airflow: Advanced (DAG viewer, logs, metrics, triggers) | Apache Flink: Basic REST API only (external UI required)
Apache Airflow: Java 8+ (optional; Python primary) | Apache Flink: Java 11+
Apache Airflow: 2014 | Apache Flink: N/A
Apache Airflow: 300+ | Apache Flink: N/A
Apache Airflow: 40-80 | Apache Flink: N/A
Apache Airflow: Requires external components (Celery/Kubernetes/RabbitMQ) | Apache Flink: N/A
Apache Airflow: 8-12+ files (scheduler, executor, database, webserver configs) | Apache Flink: N/A
Apache Airflow: Community-driven (paid support via third parties) | Apache Flink: N/A
Apache Airflow: 2014 | Apache Flink: 2011
Apache Airflow: 50,000+ | Apache Flink: N/A
Apache Airflow: Manual configuration required | Apache Flink: N/A
Apache Airflow: 350+ | Apache Flink: N/A
Apache Airflow: Limited (runtime only) | Apache Flink: N/A
Apache Airflow: 3.8+ | Apache Flink: N/A
Apache Airflow: 15,000+ | Apache Flink: N/A
Apache Airflow: 250-400 MB | Apache Flink: N/A
Apache Airflow: 30-45 minutes | Apache Flink: N/A
Apache Airflow: Yes (comprehensive web UI included) | Apache Flink: N/A
Apache Airflow: Native DAG-based automatic resolution | Apache Flink: N/A
Apache Airflow: 3 (PostgreSQL, MySQL, SQLite) | Apache Flink: N/A
Setup Complexity (Configuration Files Required)
Apache Airflow: 5-7 (airflow.cfg, DAG files, connections, secrets, logging config) | Apache Flink: N/A
Apache Airflow: 45-90 minutes with PostgreSQL + webserver setup | Apache Flink: N/A
Apache Airflow: 15+ core features (DAG visualization, execution history, logs, task duration, SLAs, alerts, backfill) | Apache Flink: N/A
Apache Airflow: 200+ officially maintained operators + community operators | Apache Flink: N/A
Enterprise Adoption (Fortune 500 Users Reported)
Apache Airflow: Airbnb, Amazon, Google, Netflix, Twitter (estimated 80+ F500) | Apache Flink: N/A
Apache Airflow: 1 (PostgreSQL backend only, no message queue required) | Apache Flink: N/A
Apache Airflow: 500-800 MB baseline | Apache Flink: N/A
Community Repository Stars (as of Feb 2025)
Apache Airflow: 35,800 GitHub stars | Apache Flink: N/A
Apache Airflow: 5,000+ | Apache Flink: N/A
Apache Airflow: PostgreSQL/MySQL required | Apache Flink: N/A
Apache Airflow: 4-8 hours | Apache Flink: N/A
Apache Airflow: Manual configuration | Apache Flink: N/A
Apache Airflow: No (third-party only) | Apache Flink: N/A
Apache Airflow: 36,000+ | Apache Flink: N/A
Apache Airflow: Task-level only (limited) | Apache Flink: N/A
Apache Airflow: Minimal (manual validation) | Apache Flink: N/A
Apache Airflow: 6-8 weeks to proficiency | Apache Flink: N/A
Apache Airflow: Astronomer Cloud (third-party) | Apache Flink: N/A
Apache Airflow: 2M+ (proven in production) | Apache Flink: N/A
Apache Airflow: 35,200 | Apache Flink: ~23,000
Apache Airflow: 320+ | Apache Flink: N/A
Apache Airflow: 2.10+ (December 2024) | Apache Flink: N/A
Apache Airflow: 45-60 (with Kubernetes/Celery) | Apache Flink: N/A
Apache Airflow: 32+ (configurable) | Apache Flink: N/A
Apache Airflow: Required (PostgreSQL/MySQL/SQLite) | Apache Flink: N/A
Apache Airflow: 12,400+ | Apache Flink: N/A
Apache Airflow: N/A | Apache Flink: 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints)
Time to First Correct Result (learning curve)
Apache Airflow: N/A | Apache Flink: 6-10
Apache Airflow: N/A | Apache Flink: Yes (core feature)
Apache Airflow: N/A | Apache Flink: 250,000
Apache Airflow: N/A | Apache Flink: 5-50ms
Apache Airflow: N/A | Apache Flink: Exactly-once (native)
Apache Airflow: N/A | Apache Flink: 500+
Apache Airflow: N/A | Apache Flink: 1M-10M
Apache Airflow: N/A | Apache Flink: 4-16 GB
Apache Airflow: N/A | Apache Flink: 2-3 nodes
Apache Airflow: N/A | Apache Flink: 4 (Java, Scala, Python, SQL)
Apache Airflow: N/A | Apache Flink: Distributed snapshots + checkpointing
Apache Airflow: N/A | Apache Flink: 23.8K
Apache Airflow: N/A | Apache Flink: Continuous streams (any size)
Apache Airflow: N/A | Apache Flink: 50-100ms
Apache Airflow: N/A | Apache Flink: Exactly-once
Apache Airflow: N/A | Apache Flink: Event-time, processing-time, session windows, custom
Apache Airflow: N/A | Apache Flink: 7/10
Apache Airflow: N/A | Apache Flink: Requires cluster with YARN/Kubernetes, moderate DevOps
Apache Airflow: N/A | Apache Flink: 8-12 weeks (with Kubernetes ops experience)
Apache Airflow: N/A | Apache Flink: 1024 MB
Apache Airflow: N/A | Apache Flink: 10,000+
Apache Airflow: N/A | Apache Flink: 4 (Java, Python, Scala, SQL)
Apache Airflow: N/A | Apache Flink: 100-500 ms (tuning dependent)
Apache Airflow: N/A | Apache Flink: Terabyte-scale (tested to 10+ TB)
Apache Airflow: N/A | Apache Flink: 29,000+
Apache Airflow: N/A | Apache Flink: 23,800+
Apache Airflow: N/A | Apache Flink: ~1,850
Apache Airflow: N/A | Apache Flink: 1.5-2.5 GB
Apache Airflow: N/A | Apache Flink: Native & first-class (core design)
Apache Airflow: N/A | Apache Flink: Limited (requires external libraries)
Apache Airflow: N/A | Apache Flink: 2015
Apache Airflow: N/A | Apache Flink: 2-3 seconds (streaming) / 4-6 (batch)
Apache Airflow: N/A | Apache Flink: ~30 native connectors
Apache Airflow: N/A | Apache Flink: ~200-400MB (optimized)
Apache Airflow: N/A | Apache Flink: Full with watermarks, out-of-order handling, allowedLateness
Apache Airflow: N/A | Apache Flink: ~1-2M events/sec
Apache Airflow: N/A | Apache Flink: Separate APIs (DataStream vs Batch)
Apache Airflow: N/A | Apache Flink: 2014
Apache Airflow: N/A | Apache Flink: 5,000,000+
Apache Airflow: N/A | Apache Flink: Java
Apache Airflow: N/A | Apache Flink: 2-4 GB
Apache Airflow: N/A | Apache Flink: 180+
Apache Airflow: N/A | Apache Flink: Memory, RocksDB, External (3 options)
Apache Airflow: N/A | Apache Flink: 0 (Open source)
Apache Airflow: N/A | Apache Flink: 10,400
Apache Airflow: N/A | Apache Flink: 12 (since 2014)
Apache Airflow: N/A | Apache Flink: Millions (1M+)
Apache Airflow: N/A | Apache Flink: 1-10ms (true event streaming)
Apache Airflow: N/A | Apache Flink: 1,000,000 - 5,000,000 (streaming-optimized)
Apache Airflow: N/A | Apache Flink: 6-8GB (efficient state management)
Apache Airflow: N/A | Apache Flink: 1,800,000 (StackOverflow, job postings 2024)
Apache Airflow: N/A | Apache Flink: 2,000+ (Flink SQL, state backends, CEP library)
Apache Airflow: N/A | Apache Flink: 18% (Alibaba, Netflix, Uber, Lyft use cases)
Apache Airflow: N/A | Apache Flink: 6-10 weeks (steeper learning curve, less documentation)
Apache Airflow: N/A | Apache Flink: 15-30 lines (native session window API)
Apache Airflow: N/A | Apache Flink: 100-500ms
Apache Airflow: N/A | Apache Flink: 2,500
Apache Airflow: N/A | Apache Flink: 15-20%
Apache Airflow: N/A | Apache Flink: 256-512MB
Apache Airflow: N/A | Apache Flink: 5 types (keyed, operator, broadcast, queryable, custom)
Apache Airflow: N/A | Apache Flink: 8,000-12,000
Apache Airflow: N/A | Apache Flink: 1-10ms (streaming native)
Apache Airflow: N/A | Apache Flink: 7-9 (complex)
Apache Airflow: N/A | Apache Flink: 2048-8192
Apache Airflow: N/A | Apache Flink: Terabytes of distributed state native
How to choose between Apache Airflow and Apache Flink
Apache Airflow vs Apache Flink — Structured Comparison Data
Machine-readable comparison dataset for Apache Airflow vs Apache Flink. Contains 165 attribute comparisons, 5 FAQ pairs.
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.
How to Choose Between Apache Airflow and Apache Flink
Primary Use Case: Apache Airflow has Workflow orchestration and task scheduling, while Apache Flink has Real-time stream processing and event streaming.
Processing Model: Apache Airflow has Batch-oriented with DAG-based scheduling, while Apache Flink has Continuous streaming with millisecond latency.
Latency: Apache Airflow has Minutes to hours (task execution overhead), while Apache Flink has Sub-second to milliseconds (true streaming).
State Management: Apache Airflow has Limited native state capabilities, while Apache Flink has Advanced distributed state with fault tolerance.
Learning Curve: Apache Airflow has Moderate (Python-based, intuitive DAG syntax), while Apache Flink has Steep (requires deep understanding of streaming concepts).
Community Size: Apache Airflow has ~25,000+ GitHub stars, larger adoption in enterprises, while Apache Flink has ~23,000+ GitHub stars, growing in tech companies.
Cost at Scale: Apache Airflow has Lower infrastructure costs for batch workloads, while Apache Flink has Higher infrastructure due to always-on clustering.