RDDs, or Resilient Distributed Datasets, are a fundamental data structure in Apache Spark, designed for distributed data processing. They allow users to work with large datasets across a cluster of computers while providing fault tolerance. RDDs can be created from existing data in storage or by transforming other RDDs. One of the key features of RDDs is their ability to support in-memory computation, which significantly speeds up data processing tasks. Users can perform various operations on RDDs, such as filtering, mapping, and reducing, making them versatile for different data analysis needs in big data applications.