The data model used by Spark is named Resilient Distributed Dataset (RDD), which is a distributed collection of elements that can be processed in parallel. An RDD can be created from an existing collection (a Python list, for example) or from an external dataset, which is stored as a file on the local machine, HDFS, or other sources.
Starting with PySpark
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