Stream Processing

Stream processing is a computing paradigm that involves the real-time processing of continuous data streams. Unlike traditional batch processing, which handles data in large chunks at scheduled intervals, stream processing allows for immediate analysis and response to incoming data. Key characteristics include low latency, high throughput, and the ability to handle large volumes of data in real-time. Common use cases include real-time analytics, monitoring applications, and event-driven architectures, where timely insights are critical for decision-making. Technologies such as Apache Kafka and Apache Flink are often utilized to implement stream processing solutions.