Batch processing is great when you can afford to wait for a full snapshot—think nightly ETL, data warehousing, or when you need exact, consistent aggregates. It lets you run huge, optimized jobs on a static dataset, which is often more efficient than continuously ingesting. Stream processing shines when latency matters. If you need real‑time alerts, dynamic dashboards, or to react to events as they arrive, streams give you that low‑round‑trip time. Reliability can be higher too, because you can replay events if something fails, rather than redoing a whole batch. So, if you’re trading off speed for correctness and can batch, go batch. If you need to see things happen as they happen and can tolerate eventual consistency, stream.