A municipal payment portal must raise a fraud flag within a fraction of a second on each card authorization. Why is overnight MapReduce a poor primary design?
Select an answer to reveal the explanation.
Short Explanation
Fraud flags are like a bouncer checking IDs at the door—decision now, not tomorrow morning. Overnight MapReduce jobs finish too late for per-authorization latency. Streaming or online scorers handle that race; MR still helps for nightly forensics.
Full Explanation
Classic MapReduce schedules map and reduce tasks over large batches, with shuffle and disk stages that imply high throughput rather than sub-second per-event SLAs. Payment fraud that must fire inside an authorization window needs streaming or online inference paths. MapReduce remains useful for offline model training or forensic batch reviews, but not as the primary real-time gate.