How does Spark handle fault tolerance in distributed data processing?
Spark ensures fault tolerance using its RDD lineage, allowing it to recompute lost data from the lineage in the event of a failure.
Thatâ€™s correct. RDDs' lineage information allows Spark to recover lost data by recomputing it from the original data source.
Can you explain how the concept of 'checkpointing' works in Spark and when you might use it?