What is the difference between cache and persist in Spark?

In Spark, cache() is a convenience method that stores a DataFrame or RDD in memory using the default storage level (MEMORY_ONLY). persist() allows you to specify a storage level such as MEMORY_ONLY, MEMORY_AND_DISK, DISK_ONLY, etc. cache() is essentially a shortcut for persist(StorageLevel.MEMORY_ONLY). Use cache() when you want quick in‑memory caching and are okay with recomputation if memory is insufficient. Use persist() when you need to control the storage level, persist to disk, or change the level after the first action. persist() can be called multiple times to change the level. In practice, cache() is used for simple caching scenarios, while persist() is used for more complex persistence requirements.