Apache Airflow
by Apache Software Foundation
Programmatic authoring, scheduling, and monitoring of data workflows Apache Airflow is an open-source platform for authoring, scheduling, and monitoring data pipelines and workflows. It was created at Airbnb in 2014 by Maxime Beauchemin, entered the Apache Software Foundation incubator in 2016, and graduated to a top-level Apache project in 2019.
Performance Scores
6 rankings evaluated
Score range: 7.2 – 8.2
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#1Best Automation Tools for Data Teams in 2026
Score: 8.0 · Best for: Complex DAG orchestration with Python-native teams
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#2Best Open-Source Workflow Engines for Engineers in 2026
Score: 8.2 · Best for: Data teams needing a battle-tested orchestrator with broad integration coverage for scheduled ETL and ML pipelines
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#3Best Process Orchestration Platforms 2026
Score: 7.7 · Best for: Data engineering teams needing DAG-based pipeline scheduling
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#3Best Durable Workflow Engines for Production in 2026
Score: 8.0 · Best for: Data platform teams orchestrating scheduled batch DAGs across warehouses and lakes
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#8Best Data Integration Platforms in 2026
Score: 8.0 · Best for: Data engineering teams running batch ETL/ELT pipelines on Python DAGs
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#8Best Open Source Automation Platforms 2026
Score: 7.2 · Best for: Data engineering teams needing a Python-native, open-source workflow orchestrator for scheduled data pipelines
Key Facts
product
| Attribute | Value | As of | Source |
|---|---|---|---|
| Airflow 3.0 release | Airflow 3.0 reached general availability on 22 April 2025: React/FastAPI UI, DAG versioning, event-driven scheduling, Task Execution API | May 2026 | Apache Airflow 3.0 announcement |
General
| Attribute | Value | As of | Source |
|---|---|---|---|
| Airflow 2 end-of-life | Airflow 2 reached end-of-life on 22 April 2026 (no further security patches); 2.11 is the bridge release for migration to Airflow 3 | Jul 2026 | Astronomer |
| Current Version | Apache Airflow 3.3.0 (released 6 July 2026) | Jul 2026 | Apache Airflow blog |
| Origin | Created at Airbnb in 2014, Apache top-level project | May 2026 | Official Website |
| GitHub Stars | 46,000+ | Jul 2026 | GitHub API |
| Contributors | 3,600+ contributors on GitHub | Jul 2026 | Astronomer State of Apache Airflow 2026 press release |
| Operators | 1,700+ community-maintained operator, hook, and sensor modules | Jul 2026 | Apache Airflow Registry |
| ASF Status | Apache Software Foundation Top-Level Project since January 2019 | Jul 2026 | Apache Airflow documentation (Project page) |
| Managed Services | Cloud-managed options: Astronomer, AWS MWAA, Google Cloud Composer, Apache Airflow jobs in Microsoft Fabric (Azure Data Factory Managed Airflow retired for new instances January 1, 2026) | Jul 2026 | Microsoft Learn (archived ADF Workflow Orchestration Manager docs) |
| Built-in Operators | 100+ official provider packages supplying built-in operators for databases, cloud services, and APIs | Jul 2026 | Apache Airflow Registry |
| Monthly Downloads | 20M+ PyPI downloads per month | Jul 2026 | PyPI Stats |
pricing
| Attribute | Value | As of | Source |
|---|---|---|---|
| Airflow cost (July 2026) | Apache Airflow is free and open source under the Apache License 2.0 (verified July 2026) — no license fee; costs are self-hosting infrastructure ($50-500/mo VM, or $200-2,000/mo on Kubernetes) or a managed service. Managed rates: AWS MWAA Small $0.49/hr and Large $0.99/hr (plus per-worker fees); Astronomer Astro from $0.35-0.42/hr per deployment; Google Cloud Composer consumption-based. | Jul 2026 | Apache Airflow / managed services |
technical
| Attribute | Value | As of | Source |
|---|---|---|---|
| Managed services (May 2026) | Main managed offerings: Astronomer Astro, Amazon MWAA, and Google Cloud Composer | May 2026 | Apache Airflow ecosystem page |
business
| Attribute | Value | As of | Source |
|---|---|---|---|
| License and origin | Open-source under the Apache 2.0 license; created at Airbnb in 2014, a top-level Apache project since 2019 | May 2026 | Apache Airflow project site |
Strengths
- ●Python-native DAG definitions with full programmatic control
- ●Largest community and plugin ecosystem in data orchestration
- ●Managed service options from Astronomer and cloud providers
- ●Proven at scale handling thousands of concurrent DAG runs
- ●Over 1,000 provider plugins cover virtually every cloud data source and destination
- ●46,285 GitHub stars and 4,509 distinct commit authors as of July 2026
- ●Mature Kubernetes executor supports horizontal worker scaling
- ●Apache Software Foundation governance provides long-term project stability
- ●Apache-2.0 under ASF governance with 4,509 distinct commit authors
- ●Deep provider ecosystem covering cloud, database and SaaS operators
- ●Airflow 3 added DAG versioning and event-driven scheduling via Data Assets
- ●Managed options from Astronomer, AWS MWAA and Google Cloud Composer
- ●De facto standard for batch data DAGs with Airbnb, Lyft, and Netflix in production
- ●Apache 2.0 licence and a vast operator ecosystem covering most data tools
- ●Managed offerings on AWS, Google Cloud, and Astronomer remove ops burden
- ●Long-tail of community examples, blog posts, and conference talks for almost every use case
- ●Largest community in workflow orchestration
- ●Python-native DAG authoring
- ●Three major managed clouds available
- ●Mature operator ecosystem
- ●Massive contributor community and plugin ecosystem
- ●Python-native DAG definitions for developer flexibility
- ●Extensive provider packages for cloud services and databases
Limitations
- ●Steep learning curve for teams without Python experience
- ●Self-hosted deployments require dedicated DevOps resources
- ●UI is functional but not visually intuitive for non-engineers
- ●No native data quality or transformation features
- ●Scheduler-centric architecture means short-running tasks have notable latency overhead
- ●DAGs are defined statically — dynamic workflow shapes require DAG factories or TaskFlow API patterns
- ●Operational complexity at scale (scheduler, webserver, workers, metadata DB, message broker)
- ●Python-only authoring
- ●No saga or compensation primitives; failure handling is retries and callbacks
- ●Scheduler tuning becomes its own discipline at high DAG counts
- ●Designed around scheduled data pipelines rather than long-running business processes
- ●DAG model fits batch data pipelines better than long-running stateful workflows
- ●Python-only authoring; not designed for cross-language back-end orchestration
- ●Self-hosting at scale requires careful scheduler and metastore tuning
- ●DAG model less suited to real-time streaming
- ●Self-host operations complex at scale
- ●UI feels dated against newer entrants
- ●Steeper learning curve than visual workflow builders
- ●Resource-intensive for self-hosting
- ●Not designed for event-driven real-time workflows
Based on evaluations in 6 rankings: Best Automation Tools for Data Teams in 2026, Best Open-Source Workflow Engines for Engineers in 2026, Best Process Orchestration Platforms 2026, Best Durable Workflow Engines for Production in 2026, Best Data Integration Platforms in 2026, Best Open Source Automation Platforms 2026
Pricing Plans
Open Source
Free and open-source, self-hosted only
- ✓Unlimited DAGs and task executions
- ✓Python-native pipeline authoring
- ✓Extensive operator and provider ecosystem
- ✓Built-in web UI for monitoring
- ✓Scheduling and dependency management
- ✓Community support via mailing list and GitHub
- !Self-hosted only
- !You manage infrastructure and upgrades
- !No commercial support
About Apache Airflow
Apache Airflow is an open-source platform for authoring, scheduling, and monitoring data pipelines and workflows. It was created at Airbnb in 2014 by Maxime Beauchemin, entered the Apache Software Foundation incubator in 2016, and graduated to a top-level Apache project in 2019. It is distributed under the Apache 2.0 license and is the de facto open-source standard for batch data orchestration.
Workflows in Airflow are defined as Directed Acyclic Graphs, or DAGs, written in Python. Each DAG describes a set of tasks and the dependencies between them; tasks are units of work such as running a SQL query, calling an API, or triggering a Spark job. Defining pipelines in code rather than in a graphical editor is the deliberate design choice that made Airflow popular with data engineers: pipelines are version-controlled, reviewed, and tested like any other software, and the large Provider ecosystem supplies pre-built operators for databases, cloud services, and data tools.
An Airflow deployment is a set of cooperating components rather than a single process. The scheduler decides when DAGs and tasks should run, the metadata database records the state of every run, an executor and its workers carry out the tasks, and a web server hosts the user interface. Production deployments commonly run these on Kubernetes for horizontal scaling. The component model is also the source of Airflow's main operational cost: a self-hosted Airflow is a distributed system that a team has to run, patch, and monitor.
flowchart LR
A[DAG files: Python] --> B[Scheduler]
B --> C[(Metadata Database)]
B --> D[Executor]
D --> E[Workers]
E --> C
C --> F[Web Server / UI]
E --> G[(Data sources, warehouses, APIs)]
Airflow 3.0 reached general availability on 22 April 2025 as the largest release in the project's history; the 3.x line reached 3.3.0 in July 2026 (stateful tasks, Java and Go task SDKs), and Airflow 2 reached end-of-life on 22 April 2026. It replaced the legacy Flask-based interface with a React front end backed by a FastAPI service, and added long-requested capabilities including DAG versioning, where a run completes against the DAG version it started on even if the file changes mid-run, and event-driven scheduling that lets external events trigger DAGs rather than only time-based or dataset-based schedules. A new Task Execution API moves Airflow toward a client-server architecture, opening the door to multi-language task SDKs beyond Python.
Airflow is free, but most organisations consume it through a managed service to avoid running the distributed system themselves. The main commercial options are Astronomer's Astro platform, Amazon Managed Workflows for Apache Airflow (MWAA), and Google Cloud Composer. Airflow competes with code-first orchestrators such as Prefect, Dagster, and Temporal, and with the warehouse-native scheduling features built into platforms like dbt Cloud and Snowflake. Its enduring advantage is ubiquity: it has the largest community, the deepest Provider ecosystem, and the widest hiring pool of any orchestrator.
Editor's Note: Across 9 ShadowGen data-engineering engagements that touched Airflow in 2024-26, the number that surprises clients is the run cost of self-hosting. A team that "just wants the free tool" is signing up to operate a scheduler, a metadata database, an executor, and workers, which in our experience is roughly 0.5 of a full-time engineer once monitoring and upgrades are counted. For teams under a few hundred DAGs we now default to a managed Airflow (Astro, MWAA, or Composer); the licence saving on self-hosting is usually smaller than the salaried time it consumes. Airflow 3.0's DAG versioning is the single upgrade most worth planning a migration around. — Rafal Fila, ShadowGen
Integrations (8)
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Best Automation Tools for Data Teams in 2026
A ranked list of the best automation and data pipeline tools for data teams in 2026. This ranking evaluates platforms across data pipeline quality, integration breadth, scalability, ease of use, and pricing value. Tools are assessed based on their ability to handle ETL/ELT workflows, data transformation, orchestration, and integration tasks that data engineers and analysts rely on daily. The ranking includes both dedicated data tools (Apache Airflow, Fivetran, Prefect) and general-purpose automation platforms (n8n, Make) that have developed strong data pipeline capabilities. Each tool is scored on a 10-point scale across five weighted criteria.
Best ETL & Data Pipeline Tools 2026
Our ranking of the top ETL and data pipeline tools for building reliable data workflows and transformations in 2026.
Questions About Apache Airflow
How much does Kestra cost in 2026?
Kestra's core platform is free and open-source (Apache 2.0) with unlimited flows and executions when self-hosted. As of July 2026 the Enterprise Edition is a quote-based annual subscription priced per instance, and the managed Kestra Cloud is in early access with usage-based pricing; neither publishes dollar prices, so budgeting starts with infrastructure costs plus a sales conversation.
Prefect vs Apache Airflow: which workflow orchestrator should you choose in 2026?
Apache Airflow 3.3 is the ecosystem standard: 46,000+ GitHub stars, the deepest operator ecosystem, and managed options from AWS, Google Cloud, and Astronomer — with Airflow 2 end-of-life since 22 April 2026. Prefect 3 offers a lighter Python-native model (@flow/@task, no XComs) with Prefect Cloud from $100/month, and announced its acquisition of Dagster Labs on 13 July 2026.
What are the best open-source workflow engines in 2026?
The top open-source workflow engines in 2026 are [Temporal](/tools/temporal-workflows/) (durable execution with multi-language SDKs), [Apache Airflow](/tools/apache-airflow/) (the de facto data DAG orchestrator), and [Prefect](/tools/prefect/) (modern Python-first workflow framework).
What are the best Alteryx alternatives in 2026?
As of April 2026, the leading Alteryx alternatives are Knime (open-source visual analytics), Dataiku (enterprise data science platform), Tableau Prep (Tableau-native data prep), Fivetran with dbt (modern ELT pattern), and Apache Airflow (open-source orchestration). Choice depends on whether teams need self-service analytics, ML workflows, or pure ETL.
Learn More
Kestra vs Apache Airflow (2026): Declarative YAML vs Python DAGs
Kestra and Apache Airflow are both open-source orchestration platforms, but they represent different generations of design: Airflow (2015, Apache Software Foundation) defines pipelines as Python DAGs with a vast managed-service ecosystem, while Kestra (2019 project, 2021 company) defines flows in declarative YAML on a Java engine with event-driven and realtime triggers. This comparison covers language model, triggers, ecosystem maturity, deployment, and licensing as of July 2026.
When Temporal Beat Airflow for a Fintech ETL Replay Job
Anonymized retrospective of a fintech client choosing Temporal over Apache Airflow for a multi-day ETL replay job. Replay correctness drove the decision; estimated total cost of ownership over 12 months landed at roughly $48,000 for Temporal Cloud vs $26,000 for managed Airflow, with replay determinism worth the premium for this workload.
Camunda vs Zeebe 2026: Camunda 7 Platform vs Camunda 8 Cloud-Native Engine
Zeebe is the cloud-native BPMN workflow engine that powers Camunda 8, while Camunda 7 is the mature JVM-based platform that preceded it. Both are maintained by Camunda Services GmbH. This 2026 comparison clarifies the architecture differences, feature deltas, migration considerations, and pricing between the two generations.