EMR failures are expensive in a way that's easy to measure. A node lost partway through a six-hour job costs you the compute you already spent plus the compute to run it again, and it moves every downstream deadline that depended on that output.

Most teams assume their job framework handles node loss for them. Spark and Hadoop both have fault tolerance built in, but how much it protects you depends on checkpointing, replication settings, and how the job was written. Those details vary job to job, and few teams have verified them under actual node loss rather than in theory.

Testing gives you a concrete answer for the jobs that matter most: this one recovers, that one restarts from the beginning, this other one produces output that looks complete but isn't. Knowing which is which changes how you prioritize both engineering effort and cluster spend.