Stream processing is a computing paradigm focused on real-time analysis and transformation of continuous data streams as they’re generated. Unlike traditional batch processing that handles data in chunks, stream processing operates on data in motion, enabling immediate insights and actions within milliseconds of data creation.
Core Principles
- Continuous data flow: Processes unbounded sequences of events from sources like IoT sensors, financial transactions, or application logs.
- Real-time operations: Performs filtering, aggregation, transformation, and pattern detection on-the-fly without storing entire datasets.
- Parallel architectures: Uses distributed systems and parallel computing techniques to handle high-velocity data streams efficiently.
