Dagster helps teams organize the work that produces and updates their data. Developers define Python data applications, while Dagster schedules or launches runs and makes their progress and failures visible. It is useful when data processing has grown beyond a few independent scripts and needs clearer ownership and operation. On Kubernetes, the platform and the containers carrying your own code can be deployed separately, with their versions and permissions coordinated.
Chart ownership
Dagster publishes its charts through the Dagster chart repository, with source inside the Dagster project. The chart supports separate user-code containers, configurable run launching and scheduling. Upstream recommends matching the Dagster Python package and chart versions, rather than upgrading the infrastructure independently without checking compatibility.
Before adoption
Define how user-code images reach the cluster, which service accounts execute runs and where run output and persistent instance data live. Give jobs only the credentials and permissions their datasets require. Set resource requests and concurrency limits, then test a failed run, a code-location outage and restoration of persistent state. Confirm that schedules and retries cannot accidentally duplicate business side effects. Keep the web interface behind the team's authentication and network controls.
Sources & further reading
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