Apache Spark is a unified analytics engine for large-scale data processing — known for its speed, ease of use, and sophisticated analytics capabilities. This Helm chart deploys a Spark 4.0 standalone cluster (one master plus N workers) on top of Managed Kubernetes, using upstream apache/spark container images straight from the Apache project. The master Web UI is exposed through a secure tunnel and protected by HTTP Basic Auth, so the cluster overview is reachable from your browser without exposing the cluster directly to the internet. Job submission stays on the cluster-internal port (spark://<release>-master-svc:7077), reachable from any pod in the namespace.
Container images come straight from docker.io/apache/spark — not a third-party rebuild.
Submit jobs over the standalone protocol on port 7077 from any pod in the namespace; the master coordinates a fleet of long-running workers.
Master Web UI exposed through a secure HTTP/2 tunnel with HTTP Basic Auth — set or generate a password at install time.
Worker StatefulSet supports HorizontalPodAutoscaler so the cluster grows with workload pressure.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Deploy a long-running Apache Spark standalone cluster (master + workers) on Kubernetes, with the master Web UI exposed through a secure tunnel and protected by HTTP Basic Auth.
Some applications are easier with a hand on the wheel. Talk to an architect who has deployed this in production.
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Explore the Trust centerApplication support
Provided by Apache. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
