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Apache Spark is a distributed computing framework that provides high-level abstractions for processing large-scale data sets. It offers multiple data abstractions, including RDD (Resilient Distributed Datasets), DataFrame, and Dataset. Let's discuss each of these abstractions: RDD (Resilient Distributed Dataset) : RDD is the fundamental data structure in Apache Spark. It represents an immutable distributed collection of objects. RDDs are fault-tolerant, meaning they can recover from failures during computation. RDDs provide a low-level programming interface and allow you to perform transformations (e.g., map, filter, reduce) and actions (e.g., count, collect) on distributed data. RDDs are primarily used in Spark's core API and are available in different programming languages, such as Scala, Java, Python, and R. DataFrame : DataFrame is an abstraction built on top of RDDs, introduced in Spark 1.3. It provides a structured and tabular view of data, similar to a table in a relat