Batch vs Stream Processing (Lambda/Kappa)

Batch processing computes results over a finite, bounded dataset. Stream processing computes results continuously over an unbounded, ever-arriving dataset. The two paradigms have different latency, cost, correctness, and operational profiles, and choosing wrong is one of the most expensive architectural mistakes a senior engineer can make. This lesson covers the mental model (bounded vs unbounded data, event time vs processing time, watermarks, windows), the two classical reference architectures (Lambda and Kappa), the modern unified models (Beam, Flink), and the production realities of exactly-once semantics, late data, replays, and operational complexity. The goal is to leave you able to choose batch, streaming, or a hybrid for any system, and to defend the choice in an interview.