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Answer: Pandas API on Spark can be used to incrementally refactor Pandas code to Spark, starting with small datasets and gradually moving to larger ones.
Pandas API on Spark provides a way to scale data processing pipelines from small to large datasets without significant refactoring. By using the familiar Pandas API syntax, developers can incrementally refactor their code to leverage Spark's distributed processing capabilities, starting with small datasets and gradually moving to larger ones, ensuring a smoother transition and reducing the need for a complete rewrite.
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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.
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