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Given a scenario where you need to scale your data processing pipeline from a small dataset to a large distributed dataset, explain how Pandas API on Spark can be a solution without requiring significant refactoring. Provide a detailed example.
A
Pandas API on Spark allows for direct scaling of Pandas code to Spark clusters without any changes.
B
Pandas API on Spark requires rewriting the entire codebase to leverage Spark's distributed capabilities.
C
Pandas API on Spark can be used to incrementally refactor Pandas code to Spark, starting with small datasets and gradually moving to larger ones.
D
Pandas API on Spark is not suitable for scaling data pipelines and requires a complete rewrite using native Spark APIs.
