Resilient Distributed Datasets (RDDs) are a fundamental data structure in the Apache Spark framework, designed for distributed computing. RDDs allow users to process large datasets across a cluster of computers while ensuring fault tolerance. They can be created from existing data in storage or by transforming other RDDs. One of the key features of RDDs is their ability to recover from failures. If a partition of an RDD is lost, Spark can recompute it using the original data and the transformations applied to create it. This makes RDDs a reliable choice for big data processing tasks.