Stream Processing vs Batch Processing Trade-offs

The Decision Framework: Stream processing and batch processing are complementary, not competing. The question is not which one to use, but when to use each. The choice hinges on latency requirements, complexity tolerance, and cost constraints. Latency and Freshness: Stream processing delivers continuous, low latency results. A fraud detection system needs decisions in under 200ms. A real-time dashboard needs updates within 1 to 5 seconds. Batch cannot meet these requirements because it waits to accumulate data before processing. Batch processing operates on bounded datasets at scheduled intervals (hourly, daily). It accepts higher latency (minutes to hours) in exchange for simplicity and efficiency. Daily financial reports, weekly churn analysis, and monthly forecasting do not need sub-second updates. Complexity and Mental Model: Batch is conceptually simpler. You process a finite dataset with a clear start and end. SQL queries, map reduce jobs, and data pipelines are straightforward. State is easier because you are not dealing with infinite streams. Stream processing requires thinking about unbounded data, event time versus processing time, late arrivals, windowing, and watermarks. Debugging is harder because state evolves continuously. Stateful operators like joins and aggregations require careful design to avoid memory leaks or incorrect results. Cost and Resource Utilization: Batch jobs run periodically and can scale up during execution, then scale down to zero. You pay for compute only during job runtime. For infrequent jobs (daily reports), this is extremely cost effective. Stream processing runs continuously 24/7. Even during low traffic periods, consumers and processing engines are running and consuming resources. At large scale, the cost difference is significant. Netflix and Uber run both: streaming for real-time use cases, batch for analytics and machine learning where freshness is less critical. When to Choose Streaming: Use streaming when latency matters and you need results in seconds or less. Use cases include fraud detection (under 200ms), real-time recommendations (1 to 3 seconds), operational monitoring (5 to 10 seconds), and event driven microservices (immediate reaction to domain events). Also choose streaming when you need continuous materialization of views. Instead of rebuilding a report every hour, streaming incrementally updates it as events arrive. When to Choose Batch: Use batch when latency is not critical: daily reporting, weekly analytics, monthly forecasting. Use batch for complex transformations and machine learning training where simplicity and debuggability outweigh freshness. Use batch when data arrives infrequently or in bulk uploads. Processing a nightly file drop does not benefit from streaming.