A metropolitan planning office needs last year's mobility indices recomputed overnight from archived fare-tap and sensor files. Why is classic MapReduce a strong fit?
Select an answer to reveal the explanation.
Short Explanation
Overnight recomputes are like baking a whole sheet of cookies, not plating one at a time. Classic MapReduce loves big historical scans that can finish by morning. Live per-tap flags and OLTP checkout belong to other tools.
Full Explanation
Classic MapReduce shines on batch-oriented workloads: large HDFS inputs, high throughput, and latency measured in minutes or hours rather than milliseconds. Rebuilding yearly mobility indices overnight matches that profile. Ultra-low-latency per-event scoring and transactional ticketing are poor MapReduce fits and should use streaming or OLTP systems instead.