Should we use checkpoint instead of cache/persist?

Checkpointing and caching serve different purposes in Spark. Caching (or persist) stores an RDD or DataFrame in memory (or disk) to speed up repeated actions; it is ideal for iterative algorithms where the same data is reused. Checkpointing writes the lineage of an RDD to stable storage (e.g., HDFS) to truncate the lineage graph and provide fault tolerance. Use checkpointing when the lineage is long or when you need to recover from failures without recomputing the entire lineage. Caching is not a replacement for checkpointing; they can be used together: cache for performance, checkpoint for reliability. Therefore, you should not replace cache with checkpoint; instead, choose based on whether you need speed or fault tolerance.