RDD stands for Resilient Distribution Datasets. It is a fault-tolerant collection of parallel running operational elements. The partitioned data of RDD is distributed and immutable. There are two types of datasets :
Parallelized collections : Meant for running parallelly.
Hadoop datasets : These perform operations on file record systems on HDFS or other storage systems.
RDDs are basically parts of data that are stored in the memory distributed across many nodes. RDDs are lazily evaluated in Spark. This lazy evaluation is what contributes to Spark’s speed.
