Explain the difference between DataFrame and RDD in Spark.

DataFrame and RDD (Resilient Distributed Dataset) are two fundamental abstractions in Spark:

• RDD represents an immutable distributed collection of objects, partitioned across multiple nodes. It offers low-level transformations and actions and requires manual schema management and explicit coding for optimization.
• DataFrame is a distributed collection of data organized into named columns. It provides a higher-level API with optimizations through Catalyst’s query optimizer. DataFrames support a rich set of built-in functions and offer seamless integration with Spark SQL, enabling SQL-like queries and efficient data processing.