Final answer:
The ease of use of Apache Spark compared to MapReduce is due to its in-memory computation capabilities, leading to faster processing speeds and its support for iterative algorithms and high-level APIs.
Step-by-step explanation:
The feature of Apache Spark that makes it much easier to use than MapReduce is C. Applications run in-memory. Unlike MapReduce, which writes intermediate data to disk, Spark performs computations in-memory, leading to much faster processing speeds.
Spark also supports iterative algorithms, which is beneficial for tasks like machine learning where multiple operations are performed on the same dataset. Additionally, Spark provides a rich set of high-level APIs including DataFrame and Dataset abstractions, and supports multiple languages such as Scala, Java, Python, and R, making it user-friendly for a broader audience.