Resilient Distributed Datasets
Explore the concept of Resilient Distributed Datasets (RDDs) in Spark, including their immutability, fault tolerance, and distribution across clusters. Understand how to create RDDs from collections, files, and DataFrames, and grasp the importance of transformations, actions, and lineage for efficient big data management.
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RDDs
The fundamental abstraction in Spark is the Resilient Distributed Dataset (RDD). It is a read-only (immutable) collection of objects or records, partitioned across the cluster that can be operated on in parallel. A partition can be reconstructed if the node hosting it experiences a failure. RDDs are a lower-level API, and DataFrames and Datasets compile to an RDD. The constituent records or objects within an RDD are Java, Python, or Scala objects. Anything can be stored in any format in these objects.
RDDs are a low-level API, so the Spark authors ...