A water utility wants Spark to enrich live meter readings with last year's HDFS partition history while new deltas keep arriving. How should that architecture be described?
Select an answer to reveal the explanation.
Short Explanation
Picture a night-shift dispatcher checking last year's outage binder while radios chatter with tonight's calls. Spark can pull those old HDFS shelves and still chew on the live feed at the same time.
Full Explanation
Spark commonly sits beside HDFS in municipal architectures: batch or historical partitions remain on the distributed filesystem while streaming jobs process new events. Combining both sources lets operators enrich live deltas with longer-term context. The pattern is complementary storage and processing, not a forced either-or choice.