parallelstroom
Parallelstroom is a streaming data processing paradigm focused on executing multiple data streams in parallel to achieve scalable, low-latency analytics. It combines ideas from data partitioning and parallel computation to apply operators to substreams concurrently.
In parallelstroom, an input stream is partitioned into substreams by a partitioning key or windowing strategy.
Architecture typically includes: an ingestion layer, a partitioning and routing component, a parallel runtime that executes
Advantages of parallelstroom include improved throughput, lower latency, and better resource utilization for large-scale streaming workloads.
Common applications are real-time analytics dashboards, fraud detection, anomaly detection, IoT telemetry processing, and streaming ETL
History and terminology: The term parallelstroom appears in some discussions of scalable streaming architectures. The underlying
See also: stream processing, data partitioning, windowing, backpressure, fault tolerance, event processing.