After HDFS and YARN are in place, the analytics team wants parallel batch processing over those stored blocks. How do the layers relate?
Select an answer to reveal the explanation.
Short Explanation
Pantry first, then the cooking line. HDFS holds the ingredients (blocks); YARN assigns kitchen stations; MapReduce is the parallel cooking. You do not throw out the pantry just because dinner started.
Full Explanation
HDFS provides distributed storage for input and output data, and YARN allocates resources for application workers. MapReduce uses that foundation to process blocks in parallel across the cluster. MapReduce does not replace durable HDFS storage, and HDFS remains the typical source of MapReduce inputs in the classic stack.