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Answer: Arrow allows for efficient data transfer between JVM and Python processes.
Apache Arrow enhances the Pandas API on Spark by enabling efficient data transfer between JVM and Python processes, thanks to its in-memory columnar data format. This reduces the overhead of serialization and deserialization, speeding up data operations. While Arrow's efficiency can indirectly benefit operations like joins, its primary role is not to optimize Spark SQL queries or enable non-columnar data formats, making options A, B, and D incorrect.
Author: LeetQuiz Editorial Team
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What is a key advantage of integrating Apache Arrow with the Pandas API on Spark? Select the single best answer.
A
Arrow automatically optimizes Spark SQL queries.
B
Arrow enables the use of non-columnar data formats.
C
Arrow allows for efficient data transfer between JVM and Python processes.
D
Arrow performs faster joins between DataFrames.
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