RDD
RDD (Resilient Distributed Dataset) is a fundamental data structure in Apache Spark that represents an immutable, partitioned collection of elements that can be operated on in parallel across a cluster. It provides fault tolerance through lineage information, allowing lost partitions to be recomputed, and supports in-memory computation for high performance. RDDs are the core abstraction for distributed data processing in Spark, enabling transformations and actions for batch processing tasks.
Developers should learn RDD when working with Apache Spark for large-scale data processing, especially in batch-oriented applications like ETL pipelines, data cleaning, and machine learning preprocessing. It is essential for scenarios requiring fine-grained control over data partitioning, custom serialization, or low-level optimizations, though newer Spark APIs like DataFrames are often preferred for structured data due to better performance and ease of use.