Apache Airflow helps teams run jobs that must happen in a particular order, such as collecting data before transforming it and producing a report. You describe the tasks and their dependencies in code; Airflow schedules them, tracks progress and exposes failures for investigation. It is useful when a collection of scripts needs a repeatable, visible workflow rather than a growing set of unrelated scheduled commands.
Current guidance
Apache Airflow supplies a project Helm chart; the old stable chart is a separate historical distribution.
Review the application major version, metadata database, executor, DAG delivery and secret configuration as one deployment design. An operator or chart cannot make task code safe to retry automatically.
Rehearse database migration and restore, then run representative DAGs with the intended executor. Preserve logs and task state needed for diagnosis. Treat chart changes and Airflow major upgrades as separate sources of risk.
Historical upstream link check · 2026-10-09
The recorded upstream address responded successfully (HTTP 200) on 2026-10-09. GitHub confirms that helm/charts is archived: this is a historical chart distribution, not evidence that the application itself is retired. Link availability does not certify the historical installation instructions or current security support.
Website availability is separate from project, chart and image support. Use the current guidance and primary sources on this page to assess the distribution.
Historical Kubedex content
Original publication: 2018-12-05T05:59:32+00:00. Preserved for context. Commands, versions, prices and results below reflect the original research.
Airflow is a platform to programmatically author, schedule and monitor workflows.
Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
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