A municipal data platform already runs nightly MapReduce batch jobs on HDFS and plans NoSQL serving plus Spark streaming next. How should leaders view MapReduce's place?
Select an answer to reveal the explanation.
Short Explanation
Think of a transit yard with overnight freight trains and daytime express buses—different vehicles, same city. MapReduce batch can sit beside NoSQL serving and Spark streams. You do not scrap the freight line to add buses.
Full Explanation
BDPC architectures commonly combine technologies by workload shape: MapReduce (or similar batch) for high-throughput HDFS jobs, NoSQL for flexible serving patterns, and Spark streaming for nearer-real-time processing. Coexistence is expected rather than mutual exclusion. MapReduce alone does not satisfy OLTP CAP trade-offs for checkout systems.