Apache Spark™ unifies the processing of your data in batches and real time streaming, using your preferred language: Python, SQL, Scala, Java or R. It executes fast, distributed ANSI SQL queries for dashboards and ad hoc reporting faster than most data warehouses. Users can perform Exploratory Data Analysis (EDA) on petabyte scale data without having to resort to downsampling and train machine learning algorithms on a laptop, using the same code to scale to fault tolerant clusters of thousands of machines. The Kubernetes Operator for Apache Spark, developed by Google Cloud, uses the Kubernetes operator pattern and custom resources to handle Apache Spark applications the same way as other Kubernetes workloads.
Deploy Kubernetes Operator for Apache Spark via Helm.
Manage Spark jobs declaratively with Kubernetes resources.
Handle submission, status monitoring, and cleanup through operator logic.
Run distributed Spark workloads with Kubernetes scheduling.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Deploy Kubernetes Operator for Apache Spark™ on Kubernetes.
Some applications are easier with a hand on the wheel. Talk to an architect who has deployed this in production.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
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.
