This video tutorial provides an overview of Spark transformations and actions. It explains the difference between narrow and wide transformations, highlighting how narrow transformations operate within a single partition without data shuffling, using the filter function as an example. Wide transformations, such as group by and order by, require data shuffling across partitions. The tutorial also covers actions in Spark, which are operations that do not result in a new RDD. The concepts discussed are foundational for understanding the directed acyclic graph in Spark UI.

What is a characteristic of narrow transformations in Spark?

• They require data shuffling across partitions.

• They can be computed from a single input partition.

• They always result in a new RDD.

• They are used for sorting data.

Which of the following is an example of a narrow transformation?

• Group by

• Filter

• Join

• Order by

Why do wide transformations require data shuffling?

• To reduce memory usage

• To avoid data loss

• To increase processing speed

• To ensure data is read in a specified order

What is the main purpose of shuffling in wide transformations?

• To combine related data into a new partition

• To delete unnecessary data

• To duplicate data across partitions

• To compress data for storage

What is a key feature of Spark actions?

• They do not result in a new RDD.

• They are transformations that require shuffling.

• They result in a new RDD.

• They are used to partition data.