Apache Airflow Consulting & Services for Enterprises

Our Airflow consulting covers DAG architecture and design, executor selection and scaling, CI/CD and testing strategy for DAGs, observability and alerting design, and migration from cron or legacy ETL schedulers. Every engagement is staffed by a named senior engineer with production Airflow experience.

It depends on your team's Kubernetes operational maturity, compliance requirements, and how much control you need over the underlying infrastructure. Managed platforms remove scheduler and worker ops overhead at a cost premium; self-hosting gives full control but means you own executor scaling and upgrades. We model the cost and risk of both against your workload.

CeleryExecutor runs tasks on a fixed pool of long-lived workers — simpler to operate, but worker capacity is static. KubernetesExecutor spins up a fresh pod per task, giving per-task resource isolation and scale-to-zero, at the cost of pod startup latency and more Kubernetes-specific tuning. We choose based on your task heterogeneity and scaling pattern, not by default.

This is almost always a scheduler health issue, not a DAG bug. The most common causes are max_active_runs saturation, a task stuck in queued because the executor's worker slots are consumed by zombie tasks, or DAG file parsing timing out. We diagnose scheduler heartbeat and parsing logs first, not the DAG code.

Yes. We map existing cron schedules or Pentaho job dependencies into explicit DAG structures, add proper retry and alerting semantics that cron never had, and validate output parity during a parallel-run period before decommissioning the legacy scheduler.

We build DAG-validation gates that catch import errors, cyclic dependencies, and top-level code performance issues before a DAG reaches production, combined with unit tests for custom operators and task logic. This catches the class of failures that otherwise only surface at 2 AM in prod.