The very first paper on RDD Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing described it as follows:

Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. As Spark is written in a functional programming paradigm, one of the key concepts of functional programming is immutable objects. Resilient Distributed Dataset is also an immutable dataset.

Formally, we can define an RDD as an immutable distributed collection of objects. It is the primary data type of Spark. It leverages cluster memory and is partitioned across the cluster.

The following is the logical representation of RDD:

RDDs can consist of (key, value) pairs as well. The following is the logical representation of pair of RDDs:

Also, as mentioned, RDD can be partitioned across the cluster. So the following is the logical representation of partitioned RDDs in a cluster: