distributedskalbar

Distributedskalbar is an open-source distributed computing framework designed to enable scalable processing of data-intensive workloads across clusters. It provides a unified programming model for batch and streaming tasks and emphasizes fault tolerance, resource elasticity, and interoperability with existing data ecosystems.

The design centers on a distributed runtime that coordinates a graph of tasks, a resilient scheduler, and a pluggable data plane. Tasks execute in isolated containers or process threads across worker nodes, allowing horizontal scaling. The system supports Kubernetes-based deployment and can integrate with common storage systems.

Key features include horizontal scalability, fault tolerance through lineage-based recovery and checkpointing, stateful stream processing support, and dynamic resource provisioning. It offers APIs in multiple languages (for example Python, Java, Scala) and provides a CLI and dashboard for monitoring.

The project favors a modular architecture with pluggable components for networking, storage, and scheduling. It uses a DAG execution model and supports windowed streaming operations, with configurable processing guarantees depending on configuration and sink integrations.

History and status: The project is developed by an open community under an open-source license and has attracted contributions from industry and academia. Documentation provides getting-started guides, tutorials, and examples.

See also: distributed computing, data processing frameworks, big data, and Kubernetes-based deployments.