What is the difference between batch processing and stream processing? Give an example of stream processing, such as using Apache Kafka.

Batch processing handles data in large, discrete chunks at scheduled intervals, typically using tools like Hadoop MapReduce or Spark batch jobs. It is suitable for historical analytics where latency is not critical. Stream processing, on the other hand, processes data continuously as it arrives, providing low‑latency, real‑time insights. It uses event‑driven architectures and stateful operators. An example of stream processing is using Apache Kafka as a messaging backbone, where producers publish events to topics and consumers subscribe to those topics. A Kafka Streams application can perform real‑time aggregation, filtering, or enrichment on the fly, emitting results to downstream systems. This allows use cases like fraud detection, live dashboards, or real‑time recommendation engines, where decisions must be made within milliseconds.