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Apache Airflow

Apache Airflow is an open-source platform for authoring, scheduling, and monitoring workflows as Python-defined DAGs. As of June 2026, Airflow 3.0.x is the established major release, introducing a service-oriented architecture, event-driven scheduling, and AI-native orchestration with the Edge Executor.

What it is

Apache Airflow is a workflow orchestration platform that allows users to programmatically author, schedule, and monitor workflows. Workflows are defined as Directed Acyclic Graphs (DAGs) in Python, providing a flexible and powerful way to manage complex task dependencies.

What problem it solves

Airflow turns recurring operational work into versioned workflow code with built-in retries, logging, and monitoring. It solves the problem of managing complex task dependencies and scheduling across multi-cloud and hybrid environments, providing a centralized control plane for data operations.

Where it fits in the stack

Orchestration / Enterprise Workflow Platform. It serves as the "brain" for batch and event-driven data operations. It coordinates between data ingestion, transformation (dbt), and AI/ML model execution layers.

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 external data changes or message queue events.
  • Distributed Edge Computing: Using the Edge Executor to run AI-native orchestration tasks on remote devices or specialized agent nodes.
  • Enterprise ETL/ELT: Coordinating massive data movements between warehouses and lakes with strict audit requirements.

Strengths

  • Airflow 3.0 Architecture: Decoupled DAG parsing from task execution via a new API Server, improving security and performance.
  • Python-Native: Workflows are defined as code, enabling standard software engineering practices like Git, CI/CD, and unit testing.
  • Extensive Ecosystem: Over 100+ provider packages for nearly every modern data and AI tool.
  • Mature Monitoring: Comprehensive UI for tracking task progress, viewing logs, and managing retries.
  • Edge Executor: Optimized for running tasks on decentralized infrastructure, ideal for agentic workflows.

Limitations

  • Operational Footprint: Requires a robust infrastructure (PostgreSQL, Redis, Workers) to run at scale.
  • Latency: Primarily designed for throughput; not suitable for sub-millisecond real-time response requirements.
  • Complexity: The service-oriented architecture of Airflow 3.0 adds new components to manage compared to previous versions.

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.
  • You are building AI/ML pipelines that require reliable data preparation.
  • You need to run tasks on remote or specialized hardware via the Edge Executor.

When not to use it

  • For very simple, single-step scripts where a cron job suffices.
  • If you require ultra-low latency request/response handling.
  • If you want a purely declarative YAML-based orchestrator (see Kestra).

Getting started

Docker Compose (Quickstart)

The fastest way to run Airflow 3.0 locally:

# 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)

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 2026-06-21

# Test a single task instance
airflow tasks test my_dag_id my_task_id 2026-06-21

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 — Asset-centric data orchestration.
  • Prefect — Dynamic Python workflows.
  • Argo Workflows — Kubernetes-native container orchestration.
  • Kestra — Declarative YAML orchestration.
  • Flyte — Large-scale ML orchestration.
  • n8n — For low-code intake and simple automation.

Sources / references

Contribution Metadata

  • Last reviewed: 2026-06-21
  • Confidence: high