Yes, Apache Spark provides an API for adding and managing checkpoints. Checkpointing is the process of making streaming applications resilient to failures. It allows you to save the data and metadata into a checkpointing directory. In case of a failure, the spark can recover this data and start from wherever it has stopped.
There are 2 types of data for which we can use checkpointing in Spark.
Metadata Checkpointing : Metadata means the data about data. It refers to saving the metadata to fault-tolerant storage like HDFS. Metadata includes configurations, DStream operations, and incomplete batches.
Data Checkpointing : Here, we save the RDD to reliable storage because its need arises in some of the stateful transformations. In this case, the upcoming RDD depends on the RDDs of previous batches.
