#202
B+
Rank #107
Apache Airflow
The de facto standard for programmatic workflow orchestration, letting teams define DAGs of tasks in Python with rich scheduling, retries, and monitoring. Its standout trait is battle-tested maturity at genuine internet scale across thousands of production deployments.
69.8
/ 100
⚙ Full Stack
Python application split into webserver, scheduler, and worker components, backed by Postgres for metadata and Redis/Celery (or Kubernetes Executor) for task distribution; DAGs are Python code loaded from a shared DAGs folder.
📈 Scaling Analysis
Proven at real internet scale via CeleryExecutor or KubernetesExecutor for distributing task execution across many workers, but achieving true high availability requires careful multi-component setup (scheduler HA, metadata DB tuning) — heavier operational lift than most workflow tools for the reliability it delivers.
🚀 Running on Nexlayer
Expert-tier deploy: webserver pod, scheduler pod, worker pod(s), `postgres.pod:5432`, and `redis.pod:6379` for CeleryExecutor — or use the KubernetesExecutor to spawn task pods directly, which fits Nexlayer's per-namespace Job model well. DAG files need a shared volume or git-sync sidecar across all pods.