Hi @Mado 

Yes, it is necessary to save the DataFrame into a new variable if you want to use caching to display the DataFrame. This is because caching the DataFrame can cause it to lose any data skipping that can come from additional filters added on top of the cached DataFrame, and the data that gets cached might not be updated if the table is accessed using a different identifier. Therefore, it is recommended to assign the results of Spark transformations back to a SparkDataFrame variable, similar to how you might use common table expressions (CTEs), temporary views, or DataFrames in other systems.