Better to use cache when dataframe is used multiple times in a single pipeline.

Using  cache()  and persist()  methods, Spark provides an optimization mechanism to store the intermediate computation of a Spark DataFrame so they can be reused in subsequent actions.

You do not have to cache anything to make it work. You would decide that based on whether you want to spend memory/storage to avoid recomputing the DataFrame, like when you may use it in multiple operations afterwards.