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In the context of Pandas UDFs, explain the concept of data skew and its impact on performance when working with distributed datasets in Spark. Provide an example of how you would identify and mitigate data skew in a Pandas UDF.
A
Data skew refers to an imbalance in the distribution of data across different partitions or nodes in a distributed system.
B
Data skew refers to the presence of missing or incomplete data in a dataset.
C
Data skew refers to the variability in the data values or features within a dataset.
D
Data skew is not a relevant concept when working with Pandas UDFs in Spark.
