apache-airflow
Programmatically author, schedule and monitor data pipelines
Decision gist · record as of 2026-08-14
Yes. Apache Airflow is a mature, actively maintained, production-grade orchestration platform with no known vulnerabilities, permissive licensing, and low install friction. It is well-suited for teams building data pipelines, ETL workflows, or distributed task orchestration. Install it if you need to programmatically define, schedule, and monitor workflows at scale; avoid it if you need only simple cron-like scheduling or one-off task execution.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports 3.10, 3.11, 3.12, 3.13, 3.14).
- A database backend (PostgreSQL 14+, MySQL 8.0+, or SQLite 3.15.0+) is required for production use; SQLite is suitable only for testing.
- Low install friction with a recent release (2 days old) and active maintenance.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions. Suitable for both open-source and proprietary projects.
last release 2026-08-12 (2 days) · last repo commit 2026-08-14 · 46,475 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 22,510,247 downloads/mo, #970 on PyPI
Alternatives
Verify before relying
pip install apache-airflow==3.3.1
from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime
dag = DAG('example', start_date=datetime(2026, 1, 1))
task = BashOperator(task_id='hello', bash_command='echo hello', dag=dag)- Whether the package includes all core Airflow features or if additional provider packages are needed for specific integrations
- Performance characteristics and scalability limits for large DAG deployments
- Specific memory and CPU requirements for typical production deployments
What it is and what it does
Apache Airflow is a mature, production-grade workflow orchestration platform that lets you define, schedule, and monitor data pipelines as Python code. Workflows are represented as directed acyclic graphs (DAGs) where each node is a task and edges define dependencies. The Airflow scheduler executes tasks on a pool of workers according to those dependencies, handling retries, error handling, and complex scheduling logic. It provides a web UI for monitoring pipeline execution, a command-line interface for managing DAGs, and rich logging for troubleshooting.
The package depends on apache-airflow-core and apache-airflow-task-sdk, and requires a relational database backend (PostgreSQL, MySQL, or SQLite) to store metadata and execution history. It supports modern Python versions (3.10 through 3.14) and runs on AMD64 and ARM64 platforms. Airflow is designed for teams building data pipelines, ETL workflows, and automated task orchestration at scale.
Use it for
- Schedule and monitor ETL pipelines that extract, transform, and load data between systems on a regular cadence
- Orchestrate multi-step data processing workflows with complex task dependencies and error handling
- Automate recurring operational tasks (backups, reports, data quality checks) with centralized logging and alerting
- Coordinate machine learning model training, evaluation, and deployment workflows across distributed infrastructure
- Build data integration pipelines that pull from multiple sources, transform data, and load into data warehouses
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Apache Airflow is a mature, actively maintained, production-grade orchestration platform with no known vulnerabilities, permissive licensing, and low install friction. It is well-suited for teams building data pipelines, ETL workflows, or distributed task orchestration. Install it if you need to programmatically define, schedule, and monitor workflows at scale; avoid it if you need only simple cron-like scheduling or one-off task execution.
Install
apache-airflow on PyPI
Before you install
Low install friction with a recent release (2 days old) and active maintenance. Depends on apache-airflow-core and apache-airflow-task-sdk. Actively maintained with regular commits and strong community engagement.
Requires Python 3.10 or later (supports 3.10, 3.11, 3.12, 3.13, 3.14). A database backend (PostgreSQL 14+, MySQL 8.0+, or SQLite 3.15.0+) is required for production use; SQLite is suitable only for testing.
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions. Suitable for both open-source and proprietary projects.
Quickstart
pip install apache-airflow==3.3.1
from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime
dag = DAG('example', start_date=datetime(2026, 1, 1))
task = BashOperator(task_id='hello', bash_command='echo hello', dag=dag)Verify before relying
- Whether the package includes all core Airflow features or if additional provider packages are needed for specific integrations
- Performance characteristics and scalability limits for large DAG deployments
- Specific memory and CPU requirements for typical production deployments
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release !=3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesapache-airflow-coreapache-airflow-task-sdk |
| Maintenance | Actively maintained 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 22,510,247 / month, #970 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow-3.3.1-py3-none-any.whl
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See also apache-airflow-task-sdk · dag-factory · apache-airflow-core · airflow-code-editor · apache-airflow-providers-git · astronomer-cosmos · apache-airflow-client · adagio · apache-airflow-providers-apache-beam · apache-airflow-providers-neo4j