Overview
Both crunch large volumes of data, but on different time horizons. Batch processing collects data into chunks and runs jobs periodically — nightly ETL, reports, model training — maximizing throughput and simplicity at the cost of freshness (results are hours old). Stream processing handles each event as it arrives (or in tiny windows), producing results in seconds — powering real-time dashboards, fraud detection and alerting — but it is harder to get right (state, ordering, late data, exactly-once).
Batch Processing vs Stream Processing: key differences
| Batch Processing | Stream Processing | |
|---|---|---|
| Data scope | Bounded (finite chunks) | Unbounded (continuous events) |
| Latency | Minutes to hours | Milliseconds to seconds |
| Throughput | Very high per job | High, but per-event overhead |
| Complexity | Simpler (rerun a job) | Harder (state, windows, late data) |
| Tools | Spark, Hadoop, warehouse SQL | Flink, Kafka Streams, Spark Streaming |
When to use Batch Processing
Reports, billing runs, ETL, model training and anything where hour-old results are fine and you want maximum throughput and the simplest re-run semantics.
When to use Stream Processing
Real-time needs — fraud detection, live metrics, alerting, personalization — where acting within seconds of an event is the whole point.
Verdict
Use batch when freshness can lag and simplicity and throughput matter; use streaming when low latency is the requirement. Many systems run both (the Lambda/Kappa architectures): a fast streaming path for now plus a batch path for completeness and reprocessing.
Common questions
What is the difference between batch and stream processing?
Batch processes finite chunks of data on a schedule with high throughput but high latency; stream processing handles events continuously as they arrive, with low latency. Batch suits reports and ETL; streaming suits real-time use cases.
Is stream processing replacing batch?
Not entirely. Streaming covers real-time needs, but batch remains simpler and cheaper for large periodic jobs, backfills and reprocessing. Most data platforms use both rather than choosing one.