What is the difference between batch and stream processing?

Answer

Batch processing handles large volumes of data in scheduled chunks, such as a nightly job that aggregates the previous day's sales; it is simple and efficient but introduces latency. Stream processing handles data continuously as individual events arrive, enabling near real-time results for use cases like fraud detection, monitoring, and live dashboards. Streaming frameworks such as Apache Flink, Kafka Streams, and Spark Structured Streaming manage state, windowing, and exactly-once semantics. Many architectures combine both, using streaming for low-latency needs and batch for heavy historical reprocessing.