What is a key benefit of using left joins over right joins in PySpark?

The key benefit of using left joins over right joins in PySpark is that left joins preserve all records from the left DataFrame while including matching records from the right DataFrame. This means that every entry in the left DataFrame will appear in the result set, irrespective of whether there is a corresponding match in the right DataFrame. If a match does not exist, the result will have null values for the fields from the right DataFrame.

This characteristic of left joins makes them particularly useful in scenarios where it is essential to maintain the complete set of data from the left DataFrame, such as in cases of fact tables that need to retain all observations regardless of associated dimensions or attributes in the right DataFrame. This helps analysts ensure that no important data points are lost during the merge process.

In contrast, using a right join would prioritize the right DataFrame, potentially excluding vital entries from the left DataFrame if there are no matches. Thus, the left join distinctly supports the retention of data in a structured manner, facilitating more informed analyses based on the complete dataset from the left side.