When to Choose Batch vs Stream: Decision Framework

The decision between batch and streaming is not about which is "better." It is about matching your architecture to specific latency, consistency, and cost requirements. Choose Batch When: You need complete and consistent data. Regulatory reporting, monthly revenue recognition, and executive dashboards cannot tolerate missing or approximate numbers. If your finance team reconciles transactions at month end, even a 0.1% error rate from dropped streaming events is unacceptable. Batch guarantees you process every event exactly once. Latency requirements are relaxed. If stakeholders consume data once per day in the morning, a batch job completing overnight is sufficient. Running at 3 AM when compute is cheap optimizes cost. You need complex joins across large historical ranges. Training a recommendation model on 6 months of user behavior and 200 million users requires scanning petabytes. Batch systems excel at this: they sort and partition data efficiently, leverage columnar formats, and scale to thousands of cores. Cost matters more than latency. Batch jobs can use spot instances or preemptible VMs, cutting compute costs by 60 to 80%. A job running 2 hours per day on elastic resources costs far less than a streaming system running 24/7. Choose Streaming When: Humans or automated systems must react quickly. Fraud detection, abuse prevention, and dynamic pricing require decisions in milliseconds to seconds. Blocking a stolen credit card 5 seconds after the first fraudulent charge saves thousands compared to catching it in tomorrow's batch run. Product experience degrades with delay. Real time recommendations, live feed ranking, and presence indicators ("your friend is online now") lose value after seconds. Users notice and churn if feeds feel stale. You need continuous monitoring with tight Service Level Agreements (SLAs). If you promise p99 API latency under 200 milliseconds and must alert on violations within 1 minute, batch processing every hour is too slow. Stream based anomaly detection catches issues immediately. The Hybrid Reality: Most large systems use both. A ride sharing platform runs streaming to calculate estimated time of arrival and match drivers in real time, with sub second latency. It runs batch overnight to analyze ride patterns, optimize pricing zones, and train demand forecasting models. Micro batch sits in between: processing every 1 to 5 minutes, it feels like streaming for some use cases but offers simpler semantics closer to batch. If your SLA is "dashboards update every 5 minutes," micro batch may be the sweet spot. Key Metrics for Decision Making: If over 80% of your workload is writes with few queries, batch is simpler and cheaper. If you query every event within seconds of arrival, streaming justifies the complexity. If your acceptable lag is 1 hour, neither pure streaming nor daily batch fits: consider micro batch or hybrid.