Pandas UDFs offer flexibility for complex records grouping in Spark, although performance can be hampered by excessive data movement between JVM and Python processes.
In scenarios with numerous groups and few records each, the performance suffers significantly, resembling the tiny files problem, leading to inefficiencies.
The use of Databricks on AWS and the specified configuration highlights the limitations of current setups when handling large datasets with Pandas UDFs.
While building IoT datasets, it's crucial to optimize data processing patterns to mitigate serialization/deserialization overhead and improve overall efficiency.
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