A Spark job on neighborhood sensor partitions runs as pipelined work across the cluster. How is that execution commonly described at a high level?
Select an answer to reveal the explanation.
Short Explanation
Spark chops a job into stages, then tasks that chew partitions—like a relay with lanes. It is not one lonely loop on a laptop or NameNode gossip alone. Pipeline the stages and the cluster earns its keep.
Full Explanation
Spark execution typically decomposes a job into stages and tasks that operate over partitions, enabling pipelined parallel work. Single-threaded mayoral laptop loops, partition-ignoring random cursors, and NameNode-only paths do not describe Spark’s processing mechanism.