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Answer: Refactor the pipeline to use Pandas API on Spark selectively, by identifying and replacing only the operations that benefit from distributed computing.
In this scenario, the best approach would be to refactor the pipeline to use Pandas API on Spark selectively. This involves identifying the operations in the existing code that can benefit from distributed computing and replacing them with their equivalent Pandas API on Spark operations. This allows you to leverage the distributed computing capabilities of Spark while minimizing the changes required to the existing code. Completely rewriting the pipeline with native Spark operations (option A) or using Pandas API on Spark as a drop-in replacement (option B) may not be feasible or efficient. Using a multi-threading approach (option D) may not provide the desired scalability for large datasets.
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Given a scenario where you have a data pipeline that currently uses Pandas for data manipulation, and you need to scale it to handle larger datasets. How would you approach the task of refactoring the pipeline to use Pandas API on Spark?
A
Replace all Pandas operations with their equivalent native Spark operations and rewrite the entire pipeline.
B
Use Pandas API on Spark as a drop-in replacement for Pandas, without any changes to the existing code.
C
Refactor the pipeline to use Pandas API on Spark selectively, by identifying and replacing only the operations that benefit from distributed computing.
D
Keep using Pandas for data manipulation and parallelize the pipeline using a multi-threading approach.
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