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Answer: Use Pandas API on Spark because it provides a familiar Pandas-like API and requires less refactoring of existing code.
In this scenario, using Pandas API on Spark would be a better choice. While native Spark may be faster and more efficient for large datasets, the Pandas API on Spark provides a familiar Pandas-like API, which can be beneficial for scaling data pipelines without much refactoring. This can save time and effort in adapting existing code to work with Spark. However, it is important to be aware of the potential performance trade-offs due to the usage of an InternalFrame.
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Consider a scenario where you have a large dataset that needs to be processed and analyzed using Pandas-like operations. You are given the option to use either native Spark or Pandas API on Spark. Which option would you choose and why?
A
Use native Spark because it is faster and more efficient for large datasets.
B
Use Pandas API on Spark because it provides a familiar Pandas-like API and requires less refactoring of existing code.
C
Use both native Spark and Pandas API on Spark simultaneously to take advantage of their respective strengths.
D
Use neither native Spark nor Pandas API on Spark, as they are not suitable for large datasets.
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