When Spark jobs with repartition commands still fail with shuffle-related issues, it can be very annoying to encounter persistent Spark faults even after implementing checkpointing. Spark's handling of shuffle phases and the difficulties in successfully breaking RDD lineage are frequently the causes of this mistake. Here, we investigate how to build robust Spark jobs that can process data effectively while lowering failure risks by combining checkpointing with persistence tactics, sophisticated configurations, and unit testing.