What is an RDD in Spark and what features does it provide?

RDD, short for Resilient Distributed Dataset, is the fundamental abstraction concept in Spark, representing an immutable, distributed collection of data elements. RDD possesses the following characteristics:

1. Flexibility: RDDs are immutable datasets that can easily be recomputed and rebuilt in memory.
2. Distributed: RDD enables computations to be distributed across multiple nodes for parallel processing.
3. Fault tolerance: RDD can automatically recover when nodes fail, ensuring data reliability and consistency.
4. Lazy evaluation: RDD supports lazy evaluation, meaning that the computation is only triggered when it is actually needed.
5. Persistence: RDDs support caching mechanism, which allows data sets to be stored in memory to accelerate computation speed.
6. Provide a wide range of operations: RDD supports various transformations and actions such as map, filter, reduce, collect, making it easy for users to implement complex data processing logic.

In summary, RDD is a core concept in Spark, providing efficient data processing and computational capabilities, which form a reliable foundation for distributed computing.