In a scenario where you perform eight to nine transformations on data, write the result to a CSV file, then apply one or two more transformations before writing to a downstream table, how would you structure your Spark job to avoid unnecessary recomputation?

First, chain all the initial eight to nine transformations into a single DataFrame. Persist this intermediate DataFrame using persist(StorageLevel.MEMORY_AND_DISK) so that it is materialized once. Write the persisted DataFrame to CSV. For the subsequent one or two transformations, read the CSV back into a DataFrame or, better, reuse the persisted DataFrame by applying the extra transformations directly on it. If the CSV is only a temporary artifact, you can skip writing to disk entirely and instead write the final result to the downstream table after applying all transformations. If you must write to CSV for audit or downstream systems, use the persisted DataFrame as the source for both the CSV write and the downstream write. This approach ensures that the expensive transformation chain is executed only once.