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In the context of using Pandas API on Spark, explain the concept of 'caching' and its role in optimizing the performance of data processing tasks.
A
Caching in Pandas API on Spark is not applicable, as it is a concept specific to native Spark operations.
B
Caching in Pandas API on Spark refers to storing the results of operations in memory, allowing for faster access in subsequent operations.
C
Caching in Pandas API on Spark is not useful, as the operations are always executed in a distributed manner, regardless of their complexity.
D
Caching in Pandas API on Spark refers to storing the entire DataFrame in memory, which can lead to memory issues for large datasets.
