Apache Spark Certification Practice Test

RDDs, or Resilient Distributed Datasets, are a fundamental data structure in Apache Spark that allow for distributed data processing. RDD transformations are operations that create a new RDD from an existing one without modifying the original dataset. The most recognized way of transforming RDDs is through the use of specific transformations.

These transformations serve various functions: they can map one RDD's elements to a new RDD (using map), filter out elements based on a condition (with filter), group data (using groupBy), and more. Each transformation results in the creation of a new RDD, which holds the results of the operation but retains immutability, meaning the original RDD remains unchanged. This approach supports efficient processing and enables Spark's lineage tracking for fault tolerance.

While merging, aggregations, and filtering are indeed techniques used within RDD manipulation, they fall under the broader category of transformations, which are designed to produce new RDDs. Consequently, the term 'specific transformations' encapsulates the various methods available in Spark for changing RDD structure or content effectively.