software
Airflow vs Flink 2026: Orchestration vs Stream Processing
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
Open-source workflow orchestration platform for scheduling, monitoring, and managing data pipelines
Data engineers building batch ETL pipelines, ML training workflows, scheduled reports, and teams with Python expertise requiring enterprise-grade job orchestration
Score63%
VS
Apache Flink
Distributed stream processing engine for real-time analytics with advanced state management and exactly-once semantics
Real-time analytics platforms, fraud detection systems, IoT data processing, financial trading platforms, and teams with distributed systems expertise needing microsecond-latency event processing
Score63%
Quick Answer
AI SummaryApache 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.
Our Verdict
AI-assistedChoose 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.
Community feedback
Was this verdict helpful?
Thanks — we'll use this to improve our verdicts.
Apache Airflow
8.6/10
Apache Flink
6.4/10
Choose Apache Airflow if
Best pickData engineers building batch ETL pipelines, ML training workflows, scheduled reports, and teams with Python expertise requiring enterprise-grade job orchestration
Choose Apache Flink if
Real-time analytics platforms, fraud detection systems, IoT data processing, financial trading platforms, and teams with distributed systems expertise needing microsecond-latency event processing
Track this comparison
Get notified when prices change, new specs ship, or our verdict updates.
Triggers: price change new spec verdict update
No spam. Stop anytime.
Key Differences at a Glance
- Primary Use Case:Workflow orchestration and task scheduling vs Real-time stream processing and event streaming
- Processing Model:✓ Apache Flink wins(Continuous streaming with millisecond latency vs Batch-oriented with DAG-based scheduling)
- Latency:✓ Apache Flink wins(Sub-second to milliseconds (true streaming) vs Minutes to hours (task execution overhead))
Key Facts & Figures
111 numeric metrics compared
| Metric | Apache Airflow | Apache Flink | Ratio |
|---|---|---|---|
| Monthly PyPI/Package Downloads (2024)(millions) | 2.8M | — | — |
| Time to First Pipeline (expert user)(hours) | 8-16 hours | — | — |
| Native Data Warehouse Support(platforms) | 10+ via adapters | — | — |
| Open Source Contributors(unique contributors) | 1,200+ | — | — |
| Time to Production (First Workflow)(minutes) | 120 minutes | — | — |
| Lines of Code (Basic ETL Task)(LOC) | 50-70 lines | — | — |
| Available Integrations(integrations) | 2,000+ operators | 200+ | |
| GitHub Stars (Community Indicator)(stars) | 35,000+ stars | — | — |
| Configuration as Code Simplicity(complexity score) | Complex (DAG operators) | — | — |
| GitHub Stars (Community Maturity)(stars) | 22,000+ | — | — |
| Project Age(years) | 9+ years (since 2015) | — | — |
| Supported Programming Languages (SDKs)(count) | Python (primary), Java/Go/C# (limited) | — | — |
| Pre-built Integrations/Operators(count) | 1,200+ official operators | — | — |
| Minimum Deployment Complexity(components) | 5+ (scheduler, webserver, DB, executor, metadata) | — | — |
| Time Since First Release(years) | 9 years (2015) | — | — |
| Pre-built Integrations(count) | 1,000+ | — | — |
| Estimated Learning Curve (Hours to Productivity)(hours) | 20-30 hours | — | — |
| Active Contributors (Monthly)(contributors) | 150+ | — | — |
| Native Integrations(count) | 1,800+ Providers | — | — |
| Time to First Productive Workflow(days) | 5-10 days | — | — |
| Minimum RAM Requirement(GB) | 1-2 GB | — | — |
| Annual Commit Activity(commits/year) | 500+ commits | — | — |
| Processing Latency(milliseconds) | 10,000-3,600,000 ms (10 seconds to 1 hour typical) | 1-100 ms (sub-second typical) | |
| Maximum Throughput per Node(events/second) | ~1,000-5,000 tasks/min | 100,000-1,000,000 events/sec | |
| Time to Deploy Pipeline(hours) | 5-15 minutes (quick setup) | 20-40 hours (learning + development) | |
| Minimum Java Version Required(version) | Java 8+ (optional; Python primary) | Java 11+ | |
| Initial Release(year) | 2014 | — | — |
| Market Share Adoption(%) | 68% | — | — |
| Available Providers/Integrations(count) | 300+ | — | — |
| Time to Proficiency(hours) | 40-80 | — | — |
| Minimum Setup Complexity(configuration files) | 8-12+ files (scheduler, executor, database, webserver configs) | — | — |
| First Release Year(year) | 2014 | 2011 | |
| Production Deployments (Estimated)(count) | 50,000+ | — | — |
| Provider/Integration Count(integrations) | 350+ | — | — |
| Community Slack Members(members) | 15,000+ | — | — |
| Memory Usage at Idle(MB) | 250-400 MB | — | — |
| Setup Time for Hello World(minutes) | 30-45 minutes | — | — |
| Supported Message Brokers(count) | 3 (PostgreSQL, MySQL, SQLite) | — | — |
| Setup Complexity (Configuration Files Required)(count) | 5-7 (airflow.cfg, DAG files, connections, secrets, logging config) | — | — |
| Time to Deploy First Task (Minutes)(minutes) | 45-90 minutes with PostgreSQL + webserver setup | — | — |
| Web UI Completeness(features) | 15+ core features (DAG visualization, execution history, logs, task duration, SLAs, alerts, backfill) | — | — |
| Supported Task Types / Operators(count) | 200+ officially maintained operators + community operators | — | — |
| Enterprise Adoption (Fortune 500 Users Reported)(count) | Airbnb, Amazon, Google, Netflix, Twitter (estimated 80+ F500) | — | — |
| Default Message Broker Options(count) | 1 (PostgreSQL backend only, no message queue required) | — | — |
| Minimum Memory Per Worker (MB)(MB) | 500-800 MB baseline | — | — |
| Community Repository Stars (as of Feb 2025)(stars) | 35,800 GitHub stars | — | — |
| GitHub Stars(stars) | 50,000+ | ~23,000 | |
| Active Contributors(developers) | 5,000+ | — | — |
| Enterprise Production Adoption(% of workflow orchestration users) | 72% | — | — |
| Base Setup Time(hours) | 4-8 hours | — | — |
| GitHub Stars (Community Size)(stars) | 36,000+ | — | — |
| Built-in Provider Integrations(count) | 300+ | — | — |
| First Official Release(year) | 2014 | — | — |
| Learning Curve Time (Average)(weeks) | 6-8 weeks to proficiency | — | — |
| Maximum Daily Task Executions (Tested)(tasks/day) | 2M+ (proven in production) | — | — |
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | 6-10 | |
| Available Built-in Connectors(count) | 50+ | 50+ | |
| Typical Throughput (single node)(events/sec) | 250,000 | 250,000 | |
| End-to-End Latency (p99)(milliseconds) | 5-50ms | 5-50ms | |
| State Size Capacity(GB) | 500+ | 500+ | |
| Throughput (Records/Second)(million records/sec) | 1M-10M | 1M-10M | |
| Memory Usage per Node(GB) | 4-16 GB | 4-16 GB | |
| Minimum Cluster Size(nodes) | 2-3 nodes | 2-3 nodes | |
| Supported Languages(count) | 4 (Java, Scala, Python, SQL) | 4 (Java, Scala, Python, SQL) | |
| GitHub Stars (2025)(stars) | 23.8K | 23.8K | |
| Processing Latency (end-to-end)(milliseconds) | 50-100ms | 50-100ms | |
| Setup Complexity (1-10)(complexity score) | 7/10 | 7/10 | |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | 8-12 weeks (with Kubernetes ops experience) | |
| Minimum Memory Requirement(MB) | 1024 MB | 1024 MB | |
| Production Deployments Reported(count) | 10,000+ | 10,000+ | |
| Programming Languages Supported(languages) | 4 (Java, Python, Scala, SQL) | 4 (Java, Python, Scala, SQL) | |
| Latency (p99 for simple aggregations)(milliseconds) | 100-500 ms (tuning dependent) | 100-500 ms (tuning dependent) | |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | Terabyte-scale (tested to 10+ TB) | |
| GitHub Stars (as of 2026)(stars) | 29,000+ | 29,000+ | |
| GitHub Stars (2026)(stars) | 23,800+ | 23,800+ | |
| Job Market Demand(postings) | ~1,850 | ~1,850 | |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | 1.5-2.5 GB | |
| Top-Level Apache Status(year achieved) | 2015 | 2015 | |
| Average Query Execution (1GB dataset)(seconds) | 2-3 seconds (streaming) / 4-6 (batch) | 2-3 seconds (streaming) / 4-6 (batch) | |
| Native Connectors Available(count) | ~30 native connectors | ~30 native connectors | |
| Memory Overhead (per task)(MB) | ~200-400MB (optimized) | ~200-400MB (optimized) | |
| Throughput (events/sec per node)(events/sec) | ~1-2M events/sec | ~1-2M events/sec | |
| Initial Release Year(year) | 2014 | 2014 | |
| Maximum Throughput(messages/second) | 5,000,000+ | 5,000,000+ | |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | 2-4 GB | |
| Time to Build First Pipeline(hours) | 72 | 72 | |
| Active Contributors (6-month window)(developers) | 180+ | 180+ | |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | 0 (Open source) | |
| Community GitHub Stars(stars) | 10,400 | 10,400 | |
| Years in Production(years) | 12 (since 2014) | 12 (since 2014) | |
| Built-in Connectors(count) | 15+ | 15+ | |
| Max Throughput (Typical Setup)(events/sec) | Millions (1M+) | Millions (1M+) | |
| Event Latency (Processing End-to-End)(milliseconds) | 1-10ms (true event streaming) | 1-10ms (true event streaming) | |
| Throughput Capacity(events/second/node) | 1,000,000 - 5,000,000 (streaming-optimized) | 1,000,000 - 5,000,000 (streaming-optimized) | |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | 6-8GB (efficient state management) | |
| Available Libraries & Integrations(count) | 2,000+ (Flink SQL, state backends, CEP library) | 2,000+ (Flink SQL, state backends, CEP library) | |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | 6-10 weeks (steeper learning curve, less documentation) | |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | 15-30 lines (native session window API) | |
| Minimum Achievable Latency (P99)(milliseconds) | 100-500ms | 100-500ms | |
| GitHub Stars (Popularity Indicator)(stars) | 2,500 | 2,500 | |
| Market Adoption Rate(percentage of streaming workloads) | 15-20% | 15-20% | |
| Memory Overhead per Task(megabytes (baseline)) | 256-512MB | 256-512MB | |
| ANSI SQL Compliance(percentage) | 95% | 95% | |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | 5 types (keyed, operator, broadcast, queryable, custom) | |
| Production Deployments (2026)(thousands of deployments) | 8,000-12,000 | 8,000-12,000 | |
| Year-over-Year Growth Rate(percentage) | 25% | 25% | |
| Minimum Processing Latency(milliseconds) | 1-10ms (streaming native) | 1-10ms (streaming native) | |
| Typical Cluster Setup Complexity(complexity score (1-10)) | 7-9 (complex) | 7-9 (complex) | |
| Memory Per Task (Typical)(MB) | 2048-8192 | 2048-8192 | |
| Enterprise Adoption (2024)(% of tech companies) | 32% | 32% |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Evenly matched1 tie
Apache Airflow
Attribute
Apache Flink
- Workflow orchestration and task schedulingPrimary Use CaseReal-time stream processing and event streaming
- Batch-oriented with DAG-based schedulingProcessing ModelContinuous streaming with millisecond latency(winner)
- Minutes to hours (task execution overhead)LatencySub-second to milliseconds (true streaming)(winner)
- Limited native state capabilitiesState ManagementAdvanced distributed state with fault tolerance(winner)
- Moderate (Python-based, intuitive DAG syntax)(winner)Learning CurveSteep (requires deep understanding of streaming concepts)
- ~25,000+ GitHub stars, larger adoption in enterprises(winner)Community Size~23,000+ GitHub stars, growing in tech companies
- Lower infrastructure costs for batch workloads(winner)Cost at ScaleHigher infrastructure due to always-on clustering
- Primary Use Case
Apache Airflow
Workflow orchestration and task scheduling
Apache Flink
Real-time stream processing and event streaming
- Processing Model
Apache Airflow
Batch-oriented with DAG-based scheduling
Apache Flink
Continuous streaming with millisecond latency(winner)
- Latency
Apache Airflow
Minutes to hours (task execution overhead)
Apache Flink
Sub-second to milliseconds (true streaming)(winner)
- State Management
Apache Airflow
Limited native state capabilities
Apache Flink
Advanced distributed state with fault tolerance(winner)
- Learning Curve
Apache Airflow
Moderate (Python-based, intuitive DAG syntax)(winner)
Apache Flink
Steep (requires deep understanding of streaming concepts)
- Community Size
Apache Airflow
~25,000+ GitHub stars, larger adoption in enterprises(winner)
Apache Flink
~23,000+ GitHub stars, growing in tech companies
- Cost at Scale
Apache Airflow
Lower infrastructure costs for batch workloads(winner)
Apache Flink
Higher infrastructure due to always-on clustering
Full Comparison
| Attribute | Apache Airflow | Apache Flink |
|---|---|---|
| Monthly PyPI/Package Downloads (2024)(millions) | 2.8M | — |
| Market Share Adoption(%) | 68% | — |
| Production Deployments (Estimated)(count) | 50,000+ | — |
| Enterprise Adoption (Fortune 500 Users Reported)(count) | Airbnb, Amazon, Google, Netflix, Twitter (estimated 80+ F500) | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | — |
Show 4 more attributesProduction Deployments Reported(count) 10,000+ — Enterprise Adoption Rate(% of Fortune 500) 18% (Alibaba, Netflix, Uber, Lyft use cases) — Market Adoption Rate(percentage of streaming workloads) 15-20% — Production Deployments (2026)(thousands of deployments) 8,000-12,000 — | ||
| Time to First Pipeline (expert user)(hours) | 8-16 hours | — |
| Time to First Productive Workflow(days) | 5-10 days | — |
| Setup Time for Hello World(minutes) | 30-45 minutes | — |
| Setup Complexity (1-10)(complexity score) | 7/10 | — |
| Native Data Warehouse Support(platforms) | 10+ via adapters | — |
| Minimum Python Knowledge Required(skill level) | Intermediate to Advanced | — |
| Open Source Contributors(unique contributors) | 1,200+ | — |
| GitHub Stars (Community Indicator)(stars) | 35,000+ stars | — |
| Community Repository Stars (as of Feb 2025)(stars) | 35,800 GitHub stars | — |
| GitHub Stars(stars) | 50,000+(winner) | ~23,000 |
| Active Contributors(developers) | 5,000+ | — |
Show 4 more attributesGitHub Stars (Community Size)(stars) 36,000+ — GitHub Stars (2025)(stars) 23.8K — Active Contributors (6-month window)(developers) 180+ — GitHub Stars (Popularity Indicator)(stars) 2,500 — | ||
| Core Use Case Scope(pipeline stages) | E, L, T, testing, ML, monitoring (full stack) | — |
| Supported Task Types / Operators(count) | 200+ officially maintained operators + community operators | — |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | — |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | — |
| Infrastructure Setup Complexity(DevOps hours) | High (scheduler, web server, worker, database required) | — |
| Minimum Deployment Complexity(components) | 5+ (scheduler, webserver, DB, executor, metadata) | — |
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | — |
| Deployment Complexity | Requires cluster with YARN/Kubernetes, moderate DevOps | — |
| Typical Cluster Setup Complexity(complexity score (1-10)) | 7-9 (complex) | — |
| Time to Production (First Workflow)(minutes) | 120 minutes | — |
| Lines of Code (Basic ETL Task)(LOC) | 50-70 lines | — |
| Configuration as Code Simplicity(complexity score) | Complex (DAG operators) | — |
| Available Integrations(integrations) | 2,000+ operators(winner) | 200+ |
| Pre-built Integrations/Operators(count) | 1,200+ official operators | — |
| Minimum Infrastructure Requirements(components) | 4+ (scheduler, worker, DB, broker) | — |
| Minimum RAM Requirement(GB) | 1-2 GB | — |
| Minimum Cluster Size(nodes) | 2-3 nodes | — |
| Uptime SLA (Managed Services)(percent) | Self-hosted (varies) | — |
| Fault Tolerance Method(mechanism) | Manual retry + task checkpointing | — |
| Delivery Semantics | Exactly-once (native) | — |
| Fault Tolerance Mechanism | Distributed snapshots + checkpointing | — |
| Processing Semantics | Exactly-once | — |
| Enterprise Support Availability | Community or third-party paid | — |
| Enterprise Commercial Support Available(boolean) | Yes (Astronomer, cloud providers) | — |
| Enterprise Support Plans(cost per month) | Community-driven (paid support via third parties) | — |
| GitHub Stars (Community Maturity)(stars) | 22,000+ | — |
| Project Age(years) | 9+ years (since 2015) | — |
| Time Since First Release(years) | 9 years (2015) | — |
| Initial Release(year) | 2014 | — |
| First Release Year(year) | 2014(winner) | 2011 |
| First Official Release(year) | 2014 | — |
Show 2 more attributesInitial Release Year(year) 2014 — Years in Production(years) 12 (since 2014) — | ||
| Maximum Workflow Duration(duration) | Days (practical limit) | — |
| State Backend Storage Limit(scalability) | Terabytes of distributed state native | — |
| Supported Programming Languages (SDKs)(count) | Python (primary), Java/Go/C# (limited) | — |
| Python Support Level(support quality) | Fully native (DAG definitions in pure Python) | PyFlink added in v1.11 (2020); improved in v1.14+ |
| ANSI SQL Compliance(percentage) | 95% | — |
| Pre-built Integrations(count) | 1,000+ | — |
| Built-in Data Quality Testing | External tools required | — |
| Built-in Web Dashboard | Yes (comprehensive) | — |
| Available Providers/Integrations(count) | 300+ | — |
| Built-in Data Lineage | Manual configuration required | — |
Show 8 more attributesTask Dependency Management Native DAG-based automatic resolution — Native Retry Logic(automatic backoff) Manual configuration — Watermark Support Yes (core feature) — Time Window Support Event-time, processing-time, session windows, custom — Programming Languages Supported(languages) 4 (Java, Python, Scala, SQL) — Supported Event Time Semantics Full with watermarks, out-of-order handling, allowedLateness — Batch+Stream Unified Code Separate APIs (DataStream vs Batch) — Built-in State Backends Memory, RocksDB, External (3 options) — | ||
| Type Safety & Validation | Minimal type hints, runtime validation | — |
| Type Safety Support | Limited (runtime only) | — |
| Type Safety Features | Minimal (manual validation) | — |
| Learning Curve Time (Average)(weeks) | 6-8 weeks to proficiency | — |
| Time to Build First Pipeline(hours) | 72 | — |
| Data Lineage Model | Task-centric DAGs | — |
| Cloud-Native Architecture | Requires external components (Celery/Kubernetes/RabbitMQ) | — |
| Default Message Broker Options(count) | 1 (PostgreSQL backend only, no message queue required) | — |
| Primary Implementation Language | Java | — |
| Estimated Learning Curve (Hours to Productivity)(hours) | 20-30 hours | — |
| Active Contributors (Monthly)(contributors) | 150+ | — |
| Native Integrations(count) | 1,800+ Providers | — |
| Annual Commit Activity(commits/year) | 500+ commits | — |
| Dynamic DAG Support | Yes (full support) | — |
| External Database Required | Yes (PostgreSQL/MySQL) | — |
| Processing Latency(milliseconds) | 10,000-3,600,000 ms (10 seconds to 1 hour typical) | 1-100 ms (sub-second typical)(winner) |
| Maximum Throughput per Node(events/second) | ~1,000-5,000 tasks/min | 100,000-1,000,000 events/sec(winner) |
| Memory Usage at Idle(MB) | 250-400 MB | — |
| Minimum Memory Per Worker (MB)(MB) | 500-800 MB baseline | — |
| Typical Throughput (single node)(events/sec) | 250,000 | — |
Show 14 more attributesEnd-to-End Latency (p99)(milliseconds) 5-50ms — Throughput (Records/Second)(million records/sec) 1M-10M — Processing Latency (end-to-end)(milliseconds) 50-100ms — Minimum Memory Requirement(MB) 1024 MB — Latency (p99 for simple aggregations)(milliseconds) 100-500 ms (tuning dependent) — Average Query Execution (1GB dataset)(seconds) 2-3 seconds (streaming) / 4-6 (batch) — Memory Overhead (per task)(MB) ~200-400MB (optimized) — Throughput (events/sec per node)(events/sec) ~1-2M events/sec — Maximum Throughput(messages/second) 5,000,000+ — Max Throughput (Typical Setup)(events/sec) Millions (1M+) — Event Latency (Processing End-to-End)(milliseconds) 1-10ms (true event streaming) — Throughput Capacity(events/second/node) 1,000,000 - 5,000,000 (streaming-optimized) — Minimum Achievable Latency (P99)(milliseconds) 100-500ms — Minimum Processing Latency(milliseconds) 1-10ms (streaming native) — | ||
| Time to Deploy Pipeline(hours) | 5-15 minutes (quick setup)(winner) | 20-40 hours (learning + development) |
| State Consistency Guarantee(semantic level) | At-least-once (with retries) | Exactly-once (configurable per checkpoint) |
| Integrated Web UI(rating) | Advanced (DAG viewer, logs, metrics, triggers) | Basic REST API only (external UI required) |
| Built-in UI/Dashboard | Yes (comprehensive web UI included) | — |
| Web UI Completeness(features) | 15+ core features (DAG visualization, execution history, logs, task duration, SLAs, alerts, backfill) | — |
| Minimum Java Version Required(version) | Java 8+ (optional; Python primary)(winner) | Java 11+ |
| Minimum Python Version(version) | 3.8+ | — |
| Time to Proficiency(hours) | 40-80 | — |
| Minimum Setup Complexity(configuration files) | 8-12+ files (scheduler, executor, database, webserver configs) | — |
| Provider/Integration Count(integrations) | 350+ | — |
| Built-in Provider Integrations(count) | 300+ | — |
| Available Built-in Connectors(count) | 50+ | — |
| Native Connectors Available(count) | ~30 native connectors | — |
| Built-in Connectors(count) | 15+ | — |
Show 2 more attributesDeveloper Community Size(active developers) 1,800,000 (StackOverflow, job postings 2024) — Available Libraries & Integrations(count) 2,000+ (Flink SQL, state backends, CEP library) — | ||
| Community Slack Members(members) | 15,000+ | — |
| Supported Message Brokers(count) | 3 (PostgreSQL, MySQL, SQLite) | — |
| Setup Complexity (Configuration Files Required)(count) | 5-7 (airflow.cfg, DAG files, connections, secrets, logging config) | — |
| Time to Deploy First Task (Minutes)(minutes) | 45-90 minutes with PostgreSQL + webserver setup | — |
| Managed Cloud Option Available(boolean) | No (third-party only) | — |
| Enterprise SaaS Option Available | Astronomer Cloud (third-party) | — |
| Minimum Database Setup(database requirement) | PostgreSQL/MySQL required | — |
| Base Setup Time(hours) | 4-8 hours | — |
| Enterprise Production Adoption(% of workflow orchestration users) | 72% | — |
| Native Asset Lineage Tracking | Task-level only (limited) | — |
| Maximum Daily Task Executions (Tested)(tasks/day) | 2M+ (proven in production) | — |
| State Size Capacity(GB) | 500+ | — |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | — |
| Memory Usage per Node(GB) | 4-16 GB | — |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | — |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | — |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | — |
| Memory Overhead per Task(megabytes (baseline)) | 256-512MB | — |
| Supported Languages(count) | 4 (Java, Scala, Python, SQL) | — |
| Optimal Dataset Size(GB minimum) | Continuous streams (any size) | — |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | — |
| GitHub Stars (as of 2026)(stars) | 29,000+ | — |
| GitHub Stars (2026)(stars) | 23,800+ | — |
| Job Market Demand(postings) | ~1,850 | — |
| Event-Time Support | Native & first-class (core design) | — |
| Machine Learning Capabilities(availability) | Limited (requires external libraries) | — |
| Top-Level Apache Status(year achieved) | 2015 | — |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | — |
| Community GitHub Stars(stars) | 10,400 | — |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | — |
| Year-over-Year Growth Rate(percentage) | 25% | — |
| Memory Per Task (Typical)(MB) | 2048-8192 | — |
| Enterprise Adoption (2024)(% of tech companies) | 32% | — |
Apache AirflowApache FlinkMonthly PyPI/Package Downloads (2024)(millions)
2.8M
—
Market Share Adoption(%)
68%
—
Production Deployments (Estimated)(count)
50,000+
—
Enterprise Adoption (Fortune 500 Users Reported)(count)
Airbnb, Amazon, Google, Netflix, Twitter (estimated 80+ F500)
—
Time to First Correct Result (learning curve)(weeks (team of 2))
6-10
—
Show 4 more attributes
Production Deployments Reported(count)
10,000+
—
Enterprise Adoption Rate(% of Fortune 500)
18% (Alibaba, Netflix, Uber, Lyft use cases)
—
Market Adoption Rate(percentage of streaming workloads)
15-20%
—
Production Deployments (2026)(thousands of deployments)
8,000-12,000
—
Time to First Pipeline (expert user)(hours)
8-16 hours
—
Time to First Productive Workflow(days)
5-10 days
—
Setup Time for Hello World(minutes)
30-45 minutes
—
Setup Complexity (1-10)(complexity score)
7/10
—
Native Data Warehouse Support(platforms)
10+ via adapters
—
Minimum Python Knowledge Required(skill level)
Intermediate to Advanced
—
Open Source Contributors(unique contributors)
1,200+
—
GitHub Stars (Community Indicator)(stars)
35,000+ stars
—
Community Repository Stars (as of Feb 2025)(stars)
35,800 GitHub stars
—
GitHub Stars(stars)
50,000+
~23,000
Active Contributors(developers)
5,000+
—
Show 4 more attributes
GitHub Stars (Community Size)(stars)
36,000+
—
GitHub Stars (2025)(stars)
23.8K
—
Active Contributors (6-month window)(developers)
180+
—
GitHub Stars (Popularity Indicator)(stars)
2,500
—
Core Use Case Scope(pipeline stages)
E, L, T, testing, ML, monitoring (full stack)
—
Supported Task Types / Operators(count)
200+ officially maintained operators + community operators
—
Stateful Window Operations Complexity(lines of code for session windows)
15-30 lines (native session window API)
—
State Management Capabilities(feature count)
5 types (keyed, operator, broadcast, queryable, custom)
—
Infrastructure Setup Complexity(DevOps hours)
High (scheduler, web server, worker, database required)
—
Minimum Deployment Complexity(components)
5+ (scheduler, webserver, DB, executor, metadata)
—
Minimum Operational Complexity(components to manage)
5-7 (JobManager, TaskManagers, StateBackend, Checkpoints)
—
Deployment Complexity
Requires cluster with YARN/Kubernetes, moderate DevOps
—
Typical Cluster Setup Complexity(complexity score (1-10))
7-9 (complex)
—
Time to Production (First Workflow)(minutes)
120 minutes
—
Lines of Code (Basic ETL Task)(LOC)
50-70 lines
—
Configuration as Code Simplicity(complexity score)
Complex (DAG operators)
—
Available Integrations(integrations)
2,000+ operators
200+
Pre-built Integrations/Operators(count)
1,200+ official operators
—
Minimum Infrastructure Requirements(components)
4+ (scheduler, worker, DB, broker)
—
Minimum RAM Requirement(GB)
1-2 GB
—
Minimum Cluster Size(nodes)
2-3 nodes
—
Uptime SLA (Managed Services)(percent)
Self-hosted (varies)
—
Fault Tolerance Method(mechanism)
Manual retry + task checkpointing
—
Delivery Semantics
Exactly-once (native)
—
Fault Tolerance Mechanism
Distributed snapshots + checkpointing
—
Processing Semantics
Exactly-once
—
Enterprise Support Availability
Community or third-party paid
—
Enterprise Commercial Support Available(boolean)
Yes (Astronomer, cloud providers)
—
Enterprise Support Plans(cost per month)
Community-driven (paid support via third parties)
—
GitHub Stars (Community Maturity)(stars)
22,000+
—
Project Age(years)
9+ years (since 2015)
—
Time Since First Release(years)
9 years (2015)
—
Initial Release(year)
2014
—
First Release Year(year)
2014
2011
First Official Release(year)
2014
—
Show 2 more attributes
Initial Release Year(year)
2014
—
Years in Production(years)
12 (since 2014)
—
Maximum Workflow Duration(duration)
Days (practical limit)
—
State Backend Storage Limit(scalability)
Terabytes of distributed state native
—
Supported Programming Languages (SDKs)(count)
Python (primary), Java/Go/C# (limited)
—
Python Support Level(support quality)
Fully native (DAG definitions in pure Python)
PyFlink added in v1.11 (2020); improved in v1.14+
ANSI SQL Compliance(percentage)
95%
—
Pre-built Integrations(count)
1,000+
—
Built-in Data Quality Testing
External tools required
—
Built-in Web Dashboard
Yes (comprehensive)
—
Available Providers/Integrations(count)
300+
—
Built-in Data Lineage
Manual configuration required
—
Show 8 more attributes
Task Dependency Management
Native DAG-based automatic resolution
—
Native Retry Logic(automatic backoff)
Manual configuration
—
Watermark Support
Yes (core feature)
—
Time Window Support
Event-time, processing-time, session windows, custom
—
Programming Languages Supported(languages)
4 (Java, Python, Scala, SQL)
—
Supported Event Time Semantics
Full with watermarks, out-of-order handling, allowedLateness
—
Batch+Stream Unified Code
Separate APIs (DataStream vs Batch)
—
Built-in State Backends
Memory, RocksDB, External (3 options)
—
Type Safety & Validation
Minimal type hints, runtime validation
—
Type Safety Support
Limited (runtime only)
—
Type Safety Features
Minimal (manual validation)
—
Learning Curve Time (Average)(weeks)
6-8 weeks to proficiency
—
Time to Build First Pipeline(hours)
72
—
Data Lineage Model
Task-centric DAGs
—
Cloud-Native Architecture
Requires external components (Celery/Kubernetes/RabbitMQ)
—
Default Message Broker Options(count)
1 (PostgreSQL backend only, no message queue required)
—
Primary Implementation Language
Java
—
Estimated Learning Curve (Hours to Productivity)(hours)
20-30 hours
—
Active Contributors (Monthly)(contributors)
150+
—
Native Integrations(count)
1,800+ Providers
—
Annual Commit Activity(commits/year)
500+ commits
—
Dynamic DAG Support
Yes (full support)
—
External Database Required
Yes (PostgreSQL/MySQL)
—
Processing Latency(milliseconds)
10,000-3,600,000 ms (10 seconds to 1 hour typical)
1-100 ms (sub-second typical)
Maximum Throughput per Node(events/second)
~1,000-5,000 tasks/min
100,000-1,000,000 events/sec
Memory Usage at Idle(MB)
250-400 MB
—
Minimum Memory Per Worker (MB)(MB)
500-800 MB baseline
—
Typical Throughput (single node)(events/sec)
250,000
—
Show 14 more attributes
End-to-End Latency (p99)(milliseconds)
5-50ms
—
Throughput (Records/Second)(million records/sec)
1M-10M
—
Processing Latency (end-to-end)(milliseconds)
50-100ms
—
Minimum Memory Requirement(MB)
1024 MB
—
Latency (p99 for simple aggregations)(milliseconds)
100-500 ms (tuning dependent)
—
Average Query Execution (1GB dataset)(seconds)
2-3 seconds (streaming) / 4-6 (batch)
—
Memory Overhead (per task)(MB)
~200-400MB (optimized)
—
Throughput (events/sec per node)(events/sec)
~1-2M events/sec
—
Maximum Throughput(messages/second)
5,000,000+
—
Max Throughput (Typical Setup)(events/sec)
Millions (1M+)
—
Event Latency (Processing End-to-End)(milliseconds)
1-10ms (true event streaming)
—
Throughput Capacity(events/second/node)
1,000,000 - 5,000,000 (streaming-optimized)
—
Minimum Achievable Latency (P99)(milliseconds)
100-500ms
—
Minimum Processing Latency(milliseconds)
1-10ms (streaming native)
—
Time to Deploy Pipeline(hours)
5-15 minutes (quick setup)
20-40 hours (learning + development)
State Consistency Guarantee(semantic level)
At-least-once (with retries)
Exactly-once (configurable per checkpoint)
Integrated Web UI(rating)
Advanced (DAG viewer, logs, metrics, triggers)
Basic REST API only (external UI required)
Built-in UI/Dashboard
Yes (comprehensive web UI included)
—
Web UI Completeness(features)
15+ core features (DAG visualization, execution history, logs, task duration, SLAs, alerts, backfill)
—
Minimum Java Version Required(version)
Java 8+ (optional; Python primary)
Java 11+
Minimum Python Version(version)
3.8+
—
Time to Proficiency(hours)
40-80
—
Minimum Setup Complexity(configuration files)
8-12+ files (scheduler, executor, database, webserver configs)
—
Provider/Integration Count(integrations)
350+
—
Built-in Provider Integrations(count)
300+
—
Available Built-in Connectors(count)
50+
—
Native Connectors Available(count)
~30 native connectors
—
Built-in Connectors(count)
15+
—
Show 2 more attributes
Developer Community Size(active developers)
1,800,000 (StackOverflow, job postings 2024)
—
Available Libraries & Integrations(count)
2,000+ (Flink SQL, state backends, CEP library)
—
Community Slack Members(members)
15,000+
—
Supported Message Brokers(count)
3 (PostgreSQL, MySQL, SQLite)
—
Setup Complexity (Configuration Files Required)(count)
5-7 (airflow.cfg, DAG files, connections, secrets, logging config)
—
Time to Deploy First Task (Minutes)(minutes)
45-90 minutes with PostgreSQL + webserver setup
—
Managed Cloud Option Available(boolean)
No (third-party only)
—
Enterprise SaaS Option Available
Astronomer Cloud (third-party)
—
Minimum Database Setup(database requirement)
PostgreSQL/MySQL required
—
Base Setup Time(hours)
4-8 hours
—
Enterprise Production Adoption(% of workflow orchestration users)
72%
—
Native Asset Lineage Tracking
Task-level only (limited)
—
Maximum Daily Task Executions (Tested)(tasks/day)
2M+ (proven in production)
—
State Size Capacity(GB)
500+
—
Maximum Managed State Size(TB)
Terabyte-scale (tested to 10+ TB)
—
Memory Usage per Node(GB)
4-16 GB
—
Baseline JVM Memory Overhead(GB)
1.5-2.5 GB
—
Memory Overhead (idle cluster)(GB)
2-4 GB
—
Memory Per Node(GB per 1M events/sec)
6-8GB (efficient state management)
—
Memory Overhead per Task(megabytes (baseline))
256-512MB
—
Supported Languages(count)
4 (Java, Scala, Python, SQL)
—
Optimal Dataset Size(GB minimum)
Continuous streams (any size)
—
Time to First Production Deployment(days)
8-12 weeks (with Kubernetes ops experience)
—
GitHub Stars (as of 2026)(stars)
29,000+
—
GitHub Stars (2026)(stars)
23,800+
—
Job Market Demand(postings)
~1,850
—
Event-Time Support
Native & first-class (core design)
—
Machine Learning Capabilities(availability)
Limited (requires external libraries)
—
Top-Level Apache Status(year achieved)
2015
—
Price (Self-Hosted)(USD/month)
0 (Open source)
—
Community GitHub Stars(stars)
10,400
—
Mean Time to Deploy Production Job(weeks)
6-10 weeks (steeper learning curve, less documentation)
—
Year-over-Year Growth Rate(percentage)
25%
—
Memory Per Task (Typical)(MB)
2048-8192
—
Enterprise Adoption (2024)(% of tech companies)
32%
—
Pros & Cons
10 pros·6 cons across both
Apache Airflow
+5-3
Pros
- Python-based DAG definition with intuitive syntax and excellent IDE support
- Rich web UI with real-time task monitoring, retry logic, and dependency visualization
- Mature ecosystem with 2,000+ community integrations and operators for external systems
- Low operational overhead with support for standalone and distributed deployments
- Excellent for complex scheduling rules and conditional task execution
Cons
- Not designed for sub-second latency requirements or continuous streaming scenarios
- State management is minimal; requires external databases for complex stateful operations
- Horizontal scaling is challenging; scheduler becomes bottleneck with 10,000+ tasks
Apache Flink
+5-3
Pros
- True sub-millisecond latency for continuous streaming data with event-time semantics
- Advanced distributed state with built-in fault tolerance and exactly-once processing guarantees
- Powerful stream transformations with windowing, joins, and complex event processing capabilities
- Scales linearly to thousands of nodes with automatic failover and task recovery
- Unified batch and stream processing API with consistent semantics across both modes
Cons
- Steep learning curve requiring deep understanding of streaming concepts and distributed systems
- Operational complexity with cluster management, Kubernetes integration, and debugging challenges
- Higher infrastructure costs due to continuous resource allocation and JobManager overhead
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
- W
Apache Airflow on Wikipedia (opens in new tab)
Open-source workflow orchestration platform for scheduling, monitoring, and managing data pipelines
- W
Apache Flink on Wikipedia (opens in new tab)
Distributed stream processing engine for real-time analytics with advanced state management and exactly-once semantics
Related Comparisons
12 more to explore
Apache Airflow vs Apache Flink
softwareApache Flink vs Apache Spark
softwareApache Flink vs Apache Kafka
softwareHadoop vs Apache Flink
softwareApache Flink vs Apache Storm
softwarePrefect vs Apache Airflow
softwareApache Airflow vs dbt
softwareKubeflow vs Apache Airflow
softwareApache Flink vs RisingWave
softwareApache Airflow vs Temporal
softwareDagster vs Apache Airflow
softwareApache Airflow vs Luigi
software
Related Articles
5 articles
- technology
Best Streaming Services in 2026: Top Picks for Every Budget & Interest
Navigating the crowded streaming landscape in 2026 can be overwhelming. We've tested and ranked the best streaming services that offer the most value, from Netflix's massive library to budget-friendly options like Tubi, helping you cut cable and find your perfect entertainment solution.
Read article - technology
Best Live TV Streaming Services & Plans for Spring 2026: Complete Buyer's Guide
Tired of overpaying for cable? Discover the best live TV streaming services and plans for Spring 2026, including YouTube TV's new genre-based packages starting at $55/month. Our comprehensive guide breaks down pricing, channels, and features to help you cut the cord.
Read article - technology
Philo in 2026: Streaming TV Service Review, Pricing & Reddit Community Insights
Explore Philo's evolution heading into 2026, including pricing tiers, channel lineup, and how it compares to competitors like Sling TV. Discover what the r/PhiloTV Reddit community thinks about the service's current offerings and future prospects.
Read article - technology
Best US Fighter Jets 2026: Top American Combat Aircraft Ranked
Discover the most advanced US fighter jets dominating the skies in 2026. From the legendary F-22 Raptor to the versatile F-35 Lightning II, we rank America's best combat aircraft based on performance, stealth, and air superiority capabilities.
Read article - technology
Philo in 2026: Pricing, Lineup & How It Compares to Sling TV
As we head into 2026, Philo continues to position itself as an affordable streaming alternative for cable TV lovers. Discover what Philo offers, how its pricing stacks up against competitors like Sling TV, and what the Reddit community thinks about its future.
Read article
Explore More
Related comparisons and categories
Explore Entities
More Software
AI generated