RDDs
RDDs (Resilient Distributed Datasets) are a fundamental data structure in Apache Spark, which is a distributed computing framework for big data processing. RDDs are immutable, fault-tolerant collections of objects that can be processed in parallel across a cluster of machines. They are created through transformations on existing RDDs or by loading data from external storage systems, such as Hadoop Distributed File System (HDFS) or Amazon S3. RDDs can be cached in memory to speed up iterative algorithms and can be persisted to disk for fault tolerance. RDDs support two types of operations: transformations, which create a new RDD from an existing one, and actions, which return a value to the driver program or write data to an external storage system. Examples of transformations include map, filter, and reduceByKey, while examples of actions include count, collect, and saveAsTextFile.
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