Every data processing system must decide when to process data: wait for a complete, bounded dataset and process it all at once (batch), or process each event as it arrives in a continuous stream (stream). This choice shapes latency guarantees, fault tolerance complexity, infrastructure cost, and what analytics are even possible. Understanding the trade-offs prevents the common mistake of choosing stream processing because it sounds more modern when batch would deliver the same business outcome at a fraction of the operational complexity.
Batch processing excels at high-throughput, complex transformations over large, complete datasets where latency of minutes to hours is acceptable. It is simpler to reason about, easier to test, and cheaper to operate because jobs run on a schedule rather than continuously. Stream processing targets sub-second to low-second latency, enables real-time dashboards and alerting, and handles continuously arriving data without requiring it to be collected and stored before processing. The two paradigms are not mutually exclusive — the Lambda and Kappa architectures combine them deliberately.