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Answer: Pandas API on Spark has a higher memory footprint due to the InternalFrame structure, potentially causing memory issues for large datasets.
Pandas API on Spark uses an InternalFrame structure that mimics the behavior of Pandas DataFrames, which can lead to a higher memory footprint compared to native Spark DataFrames. This can potentially cause memory issues for large datasets, as it does not fully leverage the optimizations and distributed processing capabilities of native Spark DataFrames.
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Discuss the limitations of Pandas API on Spark when compared to native Spark DataFrames. Provide specific examples and explain how these limitations can impact data processing tasks.
A
Pandas API on Spark lacks support for distributed processing, leading to slower performance for large datasets.
B
Pandas API on Spark has limited support for advanced data manipulation functions, impacting complex data processing tasks.
C
Pandas API on Spark does not support lazy evaluation, which can lead to inefficient query execution for large datasets.
D
Pandas API on Spark has a higher memory footprint due to the InternalFrame structure, potentially causing memory issues for large datasets.