RDDs (Resilient Distributed Datasets) are the fundamental data structure of Apache Spark. They are immutable, distributed collections of data, partitioned across a cluster of machines, that can be operated on in parallel. RDDs support two types of operations: transformations (e.g., map, filter) which create new RDDs, and actions (e.g., count, collect) which return a value to the driver program. RDDs are commonly used for large-scale data processing tasks, including ETL, machine learning, and real-time analytics.
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