What is the difference between cache and persist in Spark?

In Spark, cache() is a convenience method that stores an RDD or DataFrame in memory using the default storage level, which is MEMORY_ONLY. persist() is a more general method that allows you to specify any StorageLevel, such as MEMORY_AND_DISK, DISK_ONLY, or MEMORY_ONLY_SER. Cache() is essentially a shortcut for persist(StorageLevel.MEMORY_ONLY). The choice between them depends on the size of the data and fault‑tolerance requirements. If the data fits in memory and you want the fastest access, cache() is fine. If the data is larger than memory or you want a fallback to disk, use persist() with an appropriate level. Persist also lets you change the storage level after the first action, whereas cache() cannot be altered once called.