What is the difference between an RDD and a DataFrame in Apache Spark ?
Answer
An RDD (Resilient Distributed Dataset) is Spark's low-level abstraction, a distributed collection of objects with no schema structure known to Spark, programmed against with functional transformations like map and filter, giving fine-grained control but few automatic optimizations. A DataFrame adds tabular structure with typed, named columns, like a database table, letting Spark know the data's schema and apply a query optimizer, Catalyst, which reorganizes and optimizes operations before executing them. In practice, DataFrames are widely preferred today for their performance and more expressive API, RDDs remaining reserved for cases that genuinely need low-level control.
Common trap
The trap is freely mixing RDD and DataFrame operations in the same processing without measuring the cost: converting between the two representations isn't free, and dropping back to an RDD in the middle of a DataFrame pipeline deprives the Catalyst optimizer of the ability to see and optimize the whole processing end to end.