Load the entire existing dataset and the new dataset into Spark, perform a full join, and then filter out the unchanged records. This approach ensures all possible updates are considered but may not be cost-effective due to the large volume of data processed.

Load only the new dataset into Spark, perform a left join with the existing dataset, and then update the matching records. This approach may miss updates if the new dataset contains records not present in the existing dataset.

Load only the new dataset into Spark, perform a right join with the existing dataset, and then update the matching records. This approach may incorrectly process records from the existing dataset that do not have updates in the new dataset.

Load only the new dataset and the updated subset of the existing dataset into Spark, perform an inner join, and then update the matching records. This approach minimizes data redundancy and ensures only relevant data is processed.