Final answer:
RDD Lineage in Apache Spark represents the sequence of transformations applied to a Resilient Distributed Dataset (RDD), providing fault tolerance through lineage graph reconstruction if partitions are lost.
Step-by-step explanation:
The term RDD Lineage refers to b) It represents the history of transformations on a Resilient Distributed Dataset (RDD). RDD Lineage is a fundamental concept in Apache Spark because it allows Spark to perform fault tolerance through a concept known as lineage graph or RDD lineage. If any partition of an RDD is lost due to a failure, Spark uses this information to rebuild just the lost partitions.
RDD, or Resilient Distributed Dataset, is a fundamental data structure in Spark that is fault-tolerant and capable of parallel processing across many nodes in a Spark cluster. Therefore, the RDD Lineage records the sequence of operations (transformations) applied to the base RDD to arrive at the current dataset. By having the detailed lineage of transformations, Spark optimizes the execution plan and efficiently computes the results.