RDD is immutable distributed collection of objects
Users create RDDs in two ways: by loading an external dataset, or by
distributing a collection of objects (e.g., a list or set) in their
An RDD is, essentially, the Spark representation of a set of data, spread across multiple machines, with APIs to let you act on it. An RDD could come from any datasource, e.g. text files, a database via JDBC, etc.
The formal definition is:
RDDs are fault-tolerant, parallel data structures that let users explicitly persist intermediate results in memory, control their partitioning to optimize data placement, and manipulate them using a rich set of operators.
If you want the full details on what an RDD is, read one of the core Spark academic papers, Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing