Question

How is RDD in Spark different from Distributed Storage Management Can anyone help me with this

Answer 1

Some of the key differences between an RDD and Distributed Storage are as follows:

• A Resilient Distributed Dataset (RDD) is the primary abstraction of data for the Apache Spark framework.
• Distributed Storage is simply a file system which works on multiple nodes.
• RDDs store data in-memory (unless explicitly cached).
• Distributed Storage stores data in persistent storage.
• RDDs can re-compute itself in the case of failure or data loss.
• If data is lost from the Distributed Storage system it is gone forever (unless there is an internal replication system).

I hope this helps you !!