PySpark UDFs offer a powerful mechanism for applying custom transformations to data within Spark DataFrames. While they provide flexibility and code reusability, they also come with performance overhead and debugging challenges. By understanding the advantages and disadvantages of UDFs and following best practices, users can effectively leverage them to streamline their data processing workflows while ensuring optimal performance and maintainability.

Read the complete story at - https://medium.com/art-of-data-engineering/pyspark-udfs-leveraging-custom-functions-for-data-transfo...