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Apache Airflow

What it is

Apache Airflow is an open-source platform for authoring, scheduling, and monitoring workflows as Python-defined DAGs. As of May 2026, Airflow 3.0 is the current major release, introducing a service-oriented architecture, event-driven scheduling, and first-class AI inference support.

What problem it solves

Airflow turns recurring operational work into versioned workflow code with dependencies, schedules, retries, logs, and a web UI. It coordinates complex data pipelines, model refreshes, and integration tasks across multi-cloud and hybrid environments.

Where it fits in the stack

Orchestration / Enterprise Workflow Platform. It serves as the "brain" for batch and event-driven data operations.

Typical use cases

  • AI Inference Execution: Utilizing Airflow 3.0's synchronous DAG execution and ad-hoc scheduling for real-time model serving.
  • Event-Driven Pipelines: Triggering workflows based on message queue events or external data changes rather than just time.
  • Distributed Edge Computing: Using the Edge Executor to run tasks on remote devices outside the central data center.
  • Enterprise ETL/ELT: Coordinating massive data movements between warehouses (Snowflake, BigQuery) and lakes.

Strengths

  • Airflow 3.0 Architecture: Decoupled DAG parsing from task execution via the new API Server, improving security and scalability.
  • Python-Native: Workflows are code, enabling standard software engineering practices (Git, CI/CD, unit testing).
  • Extensive Ecosystem: Over 100+ provider packages for nearly every modern data tool.
  • First-Class Backfills: Managed by the scheduler with UI-based monitoring and control.

Limitations

  • Operational Footprint: Airflow 3.0 reduces but does not eliminate the need for a robust infrastructure (PostgreSQL, Redis, Workers).
  • Latency: While improved in 3.0, it is still primarily designed for throughput rather than sub-millisecond real-time response.
  • Complexity: The shift to a service-oriented architecture adds new components (API Server) to manage.

When to use it

  • You need to orchestrate complex, multi-step workflows with strict audit and retry requirements.
  • You want to leverage a mature ecosystem with enterprise-grade security and monitoring.
  • You are building AI/ML pipelines that require reliable data preparation and model refresh cycles.

When not to use it

  • For very simple, single-step scripts where a cron job or a basic Python script suffices.
  • If you require ultra-low latency request/response handling (consider a dedicated API framework).

Licensing and cost

  • Open Source: Yes (Apache License 2.0)
  • Cost: Free self-hosted; paid managed offerings (Astronomer, AWS MWAA, Google Cloud Composer).
  • Self-hostable: Yes

Getting started

Docker Compose (Quickstart)

The fastest way to run Airflow 3.0 locally is using the official community Docker Compose file.

# Download the docker-compose file
curl -LfO 'https://airflow.apache.org/docs/apache-airflow/stable/docker-compose.yaml'

# Initialize the database
docker compose up airflow-init

# Start all services
docker compose up -d

Access the UI at http://localhost:8080 (default: airflow/airflow).

Helm (Kubernetes)

For production-grade self-hosting on K3s or EKS:

helm repo add apache-airflow https://airflow.apache.org
helm upgrade --install airflow apache-airflow/airflow \
  --namespace airflow \
  --create-namespace \
  --set executor=CeleryExecutor

CLI examples

The Airflow CLI is used for managing DAGs, tasks, and the environment.

# List all active DAGs
airflow dags list

# Trigger a DAG run manually
airflow dags trigger my_inference_pipeline

# Check the status of a specific task
airflow tasks state my_inference_pipeline my_task_id 2026-05-31

# Test a single task instance without running the full DAG
airflow tasks test my_dag_id my_task_id 2026-05-31

API examples

Airflow 3.0 relies heavily on its REST API for integration.

# Health check via API Server
curl -X GET "http://localhost:8080/api/v1/health" \
     -u "airflow:airflow"

# Trigger a DAG run with configuration JSON
curl -X POST "http://localhost:8080/api/v1/dags/my_dag_id/dagRuns" \
     -u "airflow:airflow" \
     -H "Content-Type: application/json" \
     -d '{"conf": {"input_path": "s3://bucket/data.csv"}}'
  • Temporal — For durable, stateful function orchestration.
  • Dagster — For data-asset-centric orchestration.
  • Prefect — For dynamic, Python-native workflows.
  • Argo Workflows — For Kubernetes-native container orchestration.
  • Kestra — For event-driven declarative orchestration.
  • Flyte — For large-scale machine learning workflows.
  • n8n — For low-code automation and intake.
  • Prometheus — For monitoring Airflow metrics.
  • OpenTelemetry — For distributed tracing in Airflow 3.0.

Backlog

  • [x] Perform quarterly technical freshness audit. (Completed: 2026-05-31)

Contribution Metadata

  • Last reviewed: 2026-05-31
  • Confidence: high

Sources / References