Apache Airflow

Apache Airflow
Original author(s)	Maxime Beauchemin / Airbnb
Developer(s)	Apache Software Foundation
Initial release	June 3, 2015 (2015-06-03)
Stable release	3.0.1 / May 12, 2025 (2025-05-12)
Repository	github.com/apache/airflow
Written in	Python
Operating system	POSIX-compliant (macOS, Linux)
Available in	English
Type	Workflow management system
License	Apache License 2.0
Website	airflow.apache.org

Apache Airflow is an open-source workflow management platform for data engineering pipelines. Developed initially at Airbnb in October 2014, it enables users to programmatically author, schedule, and monitor workflows using Python. The platform uses directed acyclic graphs (DAGs) to manage workflow orchestration, with tasks and dependencies defined as Python code.

Overview

Apache Airflow was created to address the challenge of managing complex data workflows at scale. Unlike traditional workflow management systems that rely on XML configuration files or graphical interfaces, Airflow embraces a "configuration as code" philosophy, allowing developers to define workflows using Python scripts. This approach provides the flexibility to leverage Python's extensive ecosystem of libraries while maintaining version control and enabling collaborative development.

The platform has grown significantly since its inception, with over 30 million monthly downloads as of 2025 (a 30-fold increase since 2020) and adoption by more than 80,000 organizations worldwide. Airflow became an Apache Incubator project in March 2016 and graduated to a top-level Apache Software Foundation project in January 2019, demonstrating its maturity and community support.

Architecture

Core components

Apache Airflow's architecture consists of several key components that work together to execute and monitor workflows:

• Scheduler: Monitors DAGs and triggers task execution based on dependencies and schedules
• Executor: Manages the actual execution of tasks, with support for various execution backends
• Web server: Provides the user interface for monitoring and managing workflows
• Metadata database: Stores DAG definitions, task states, and execution history
• Worker processes: Execute individual tasks as directed by the scheduler

The platform supports multiple executor types, including LocalExecutor for single-machine deployments, CeleryExecutor for distributed execution, and KubernetesExecutor for container-based orchestration. This flexibility allows organizations to scale from small deployments to enterprise-level installations handling thousands of concurrent tasks.

Directed Acyclic Graphs (DAGs)

In Airflow, workflows are represented as DAGs, where nodes represent tasks and edges represent dependencies between tasks. This model ensures that workflows have no cycles and that task execution follows a deterministic path. Each DAG is defined in a Python file that specifies:

• Task definitions and their execution logic
• Dependencies between tasks
• Schedule intervals or triggering conditions
• Default arguments and configuration parameters
• Retry policies and error handling behavior

DAGs can be scheduled to run at regular intervals (hourly, daily, weekly) or triggered by external events such as file arrivals, API calls, or completion of upstream workflows. This flexibility makes Airflow suitable for both batch processing and event-driven architectures.

Features

Version 3.0 enhancements

Apache Airflow 3.0, released in April 2025, introduced several major features that address long-standing user requests and architectural improvements:

DAG Versioning: The most requested feature based on community surveys, DAG versioning ensures that a workflow runs to completion using the same version of code that existed when it started, even if updates are deployed during execution. This provides consistency and reproducibility for long-running workflows.

React-based User Interface: A completely redesigned web interface built with React provides improved performance, modern aesthetics, and better support for large-scale deployments. The new UI offers seamless navigation between asset-oriented and task-oriented views.

Improved Backfill Support: Backfills now run within the scheduler for better control and monitoring. Users can initiate and track backfills directly from the UI or API, with improved diagnostics and progress tracking.

Task Execution Interface: A new client-server architecture enables execution in any environment and supports multiple programming languages through Task SDKs. This represents one of the most significant architectural changes in Airflow's history.

Event-Driven Scheduling: Native support for event-driven workflows allows Airflow to react to external events from messaging systems and data platforms, enabling real-time data processing scenarios.

Core capabilities

Beyond the version 3.0 enhancements, Airflow provides comprehensive workflow management features:

Dynamic Pipeline Generation: Python-based workflow definitions enable dynamic DAG creation based on configuration files, database queries, or external APIs. This allows for patterns like creating one DAG per customer or data source without manual duplication.

Extensibility: A rich ecosystem of provider packages offers pre-built integrations with cloud platforms (AWS, GCP, Azure), databases, messaging systems, and other tools. Custom operators can be developed for proprietary systems.

Monitoring and Alerting: Built-in monitoring capabilities track task execution times, success rates, and resource usage. Integration with external monitoring systems like Prometheus and Datadog enables comprehensive observability.

Security Features: Role-based access control, authentication integration with enterprise identity providers, and encryption of sensitive data ensure secure operation in enterprise environments.

Ecosystem

Provider packages

Apache Airflow's functionality is extended through provider packages, which are maintained and released independently of the core platform. These packages include integrations for:

• Cloud platforms (AWS, GCP, Azure, Alibaba Cloud)
• Databases (PostgreSQL, MySQL, MongoDB, Cassandra)
• Data processing frameworks (Apache Spark, Apache Beam, Databricks)
• Container orchestration (Kubernetes, Docker)
• Messaging systems (Apache Kafka, RabbitMQ, Amazon SQS)
• Monitoring and alerting tools

This modular approach allows organizations to install only the integrations they need, reducing dependencies and potential security vulnerabilities.

Managed services

Several cloud providers and companies offer managed Airflow services that handle infrastructure, scaling, and maintenance:

Google Cloud Composer: A fully managed workflow orchestration service on Google Cloud Platform that integrates natively with other GCP services. It handles infrastructure provisioning, monitoring, and automatic scaling.

Amazon Managed Workflows for Apache Airflow (MWAA): Amazon Web Services' managed Airflow service, launched in November 2020, provides integration with AWS services and handles infrastructure management within AWS environments.

Astronomer: Offers both cloud-based SaaS and Kubernetes-based deployment options with additional enterprise features for monitoring, CI/CD integration, and multi-tenancy support.

Use cases

Apache Airflow is widely used across industries for various data engineering and automation scenarios:

• Data Pipeline Orchestration: ETL/ELT workflows that move and transform data between systems
• Machine Learning Pipelines: Training, validation, and deployment of machine learning models
• Business Intelligence: Scheduled report generation and data aggregation for analytics
• Infrastructure Automation: Cloud resource provisioning and configuration management
• Data Quality Monitoring: Automated data validation and alerting on data anomalies

Technical specifications

System requirements

Apache Airflow requires:

• Python 3.9, 3.10, 3.11, or 3.12
• A POSIX-compliant operating system (Linux, macOS)
• A supported database backend:
  • PostgreSQL (10+)
  • MySQL (8.0+) or MariaDB (10.3+)
  • SQLite (for development only)

Deployment options

Airflow can be deployed in various configurations:

• Standalone: Single-machine installation using LocalExecutor
• Distributed: Multi-node deployment using CeleryExecutor or KubernetesExecutor
• Containerized: Docker and Kubernetes deployments using official Helm charts
• Managed services: Cloud provider offerings that handle infrastructure

Development and community

Apache Airflow follows the Apache Software Foundation's development model with a focus on community-driven development. The project maintains:

• Over 300 active contributors
• Regular release cycles (quarterly for minor versions)
• Comprehensive documentation and tutorials
• Active mailing lists and Slack workspace
• Annual Airflow Summit conferences

The project's commitment to backward compatibility and migration support has been crucial for enterprise adoption, with clear upgrade paths and deprecation policies for major version transitions.

See also

• Workflow management system
• Apache Oozie – Hadoop workflow scheduler
• Luigi – Python-based workflow management
• Apache NiFi – Data flow automation
• Prefect – Modern workflow orchestration
• Dagster – Data orchestration platform
• Temporal – Microservice orchestration

External links

• Official website
• Apache Airflow on GitHub
• Apache Airflow on PyPI
• Official Documentation
• Airflow Ecosystem