This document discusses Resilient Distributed Datasets (RDD), a fault-tolerant abstraction in Apache Spark for cluster computing. RDDs allow data to be reused across computations and support transformations like map, filter, and join. RDDs can be created from stable storage or other RDDs, and Spark computes them lazily for efficiency. The document provides examples of how RDDs can express algorithms like MapReduce, SQL queries, and graph processing. Benchmarks show Spark is 20x faster than Hadoop for iterative algorithms due to RDDs enabling data reuse in memory across jobs.