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In the context of Pandas UDFs, explain the concept of data parallelism and its benefits when working with large datasets in Spark. Provide an example of how you would implement data parallelism in a Pandas UDF.
A
Data parallelism refers to the ability to process different partitions or chunks of data simultaneously, in parallel, across multiple nodes or cores.
B
Data parallelism refers to the ability to process the same partition or chunk of data simultaneously, in parallel, across multiple nodes or cores.
C
Data parallelism refers to the ability to process data in a distributed manner, but not necessarily in parallel.
D
Data parallelism is not a relevant concept when working with Pandas UDFs in Spark.
