Data Stream Processing
Data Stream Processing is a computing paradigm that handles continuous, real-time data flows (streams) as they are generated, rather than processing static datasets in batches. It enables low-latency analysis, transformation, and aggregation of data from sources like sensors, logs, or financial transactions. This approach is essential for applications requiring immediate insights, such as fraud detection, monitoring systems, and real-time analytics.
Developers should learn Data Stream Processing when building systems that need to react to events in real-time, such as IoT platforms, stock trading algorithms, or social media feeds. It's particularly valuable for scenarios where data volume is high and latency must be minimized, as it allows for incremental processing without waiting for complete datasets. Use cases include anomaly detection, live dashboards, and stream-based ETL pipelines.