RDD (Resilient Distributed Dataset)

A Resilient Distributed Dataset (RDD) is a core data structure in Apache Spark that allows large datasets to be processed across multiple computers efficiently. It is designed to handle big data by splitting it into smaller parts stored across different nodes in a network, enabling parallel processing. RDDs are resilient because they automatically recover lost data if a node fails, ensuring reliable computation. They provide a flexible way to perform transformations (like filtering or mapping) and actions (like counting), making large-scale data analysis faster, fault-tolerant, and scalable in distributed systems.