Resilient Distributed Dataset (RDD)

A Resilient Distributed Dataset (RDD) is a core data structure in Apache Spark that enables efficient big data processing. It is an immutable collection of objects spread across multiple computers, allowing parallel operations to be performed quickly. RDDs are resilient because they automatically recover from failures by re-computing lost data using lineage information. They provide fault tolerance, flexibility, and high performance for tasks like filtering, mapping, and aggregating large datasets, making them essential for scalable, distributed data analysis without the need for complex management of data storage.