Stream Processing vs Batch Processing

This post defines stream processing and contrasts it with batch processing, explaining the core characteristics of stream processing: continuous processing, low latency, and support for event-time processing. It details common use cases for stream processing across various industries and explains how Apache Kafka facilitates stream processing through its stream-table duality and real-time data pipelines. It also introduces Kafka Streams and ksqlDB as methods for implementing stream processing solutions.