RDD stands for Resilient Distributed Dataset, which are fault-tolerant collections of elements that can be operated on in parallel in Spark. There are two ways to create RDDs: by parallelizing an existing collection or referencing a dataset in an external storage system. RDD operations include transformations, which take an RDD as input and output one or more RDDs lazily without changing the original, and actions, which trigger computation. Transformations are either narrow, originating from a single partition, or wide, taking data from multiple partitions requiring a shuffle.

1. What is RDD?
• RDD means Resilient distributed dataset.
• Spark revolves around the concept of RDD which is a fault-
tolerant collection of elements that can be operated in parallel.
• There are two ways to create RDDs, it can be created by
parallelizing an existing collection in your driver program, or
referencing a dataset in an external storage system such as
(HDFS, Hbase, or any datasource offering Hadoop format)

2. RDDs & its Operations:-
• There are basically two types of RDDs operations in spark.
1. Transformations.
2. Actions.

3. Transformations
• The RDD transformations are some functions that takes one
RDD as input and form one or more than one RDD as an
output .
• As all RDDs are immutable then the main RDD will not be
changed.
• It is lazy operation though it creates some RDDs but they can
executes when an action is called.

4. Types of RDD Transformation:
• To improve the computation performance, we can set some
transformations as pipelined. It helps to optimize process.
• There are two kinds of transformations:
1. Narrow Transformation
2. Wide Transformation

5. Narrow Transformation
• Narrow transformations are
generated as a result of
Map, Filter or these kind of
operations
• It originates from a single
partition in a parent RDD .
Only some partitions are
used to find result.

6. Wide Transformation
• Wide Transformations are
generated as a result of
GroupBykey(),
ReduceBykey() or these kind
of operations.
• In these case to form a data
partition, it can take data from
more than one partitions.
• It is also known as shuffle
partition.

7. Thank You