DeadManCheck provides enhanced monitoring for Apache Airflow Directed Acyclic Graphs (DAGs), addressing gaps left by Airflow's built-in alerting. While Airflow can notify users when a DAG fails, it does not detect cases where a DAG completes successfully without performing meaningful work—such as exporting zero rows, syncing no records, or producing no files. DeadManCheck is designed to identify these silent successes and alert users when a DAG runs but processes nothing, or when schedule anomalies and duration drifts occur.
The platform offers several integration options for Airflow users. It supports configuration via DAG-level callbacks, allowing users to trigger DeadManCheck alerts when a DAG succeeds or fails. By sending a count of processed items with each ping, users can set output assertions that trigger alerts if the count falls below a specified threshold. This feature is particularly useful for data pipeline jobs, export and sync operations, and report generation tasks, ensuring that even successful runs that accomplish nothing are flagged. DeadManCheck also enables duration monitoring by tracking how long each DAG run takes and alerting when a run exceeds twice the rolling average duration, helping users catch slowdowns and schedule drift early.
For integration, DeadManCheck can be used with Airflow's PythonOperator or HttpOperator, and can be connected via Airflow's Connections UI. It also supports monitoring for a variety of scheduled jobs beyond Airflow, including cron jobs, ETL jobs, backups, Celery tasks, GitHub Actions, Kubernetes CronJobs, and more.
Pricing includes a free tier for up to five monitors and a paid plan at $29 per month for unlimited monitors. There is also an option to self-host the service for free. DeadManCheck positions itself within the observability and job monitoring class, with a focus on output assertions and duration anomaly detection that other cron monitoring tools do not provide.
Apache Airflow DAG Monitoring is an Observability & monitoring product. It focuses on detecting silent failures and schedule anomalies in Airflow DAGs that are not caught by default alerting mechanisms. Apache Airflow DAG Monitoring is a B2B product aimed at data engineers and Airflow administrators. A free plan is available; paid tiers begin at $19. It runs on the web.
Behind Apache Airflow DAG Monitoring is DeadManCheck, and the product first shipped in 2026. The project is developed in the open on GitHub with 119 commits in the last 90 days. Among its 5 catalogued features are DAG monitoring, silent failure detection, and schedule anomaly alerts. It exposes integrations via a public API.
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