Spark Programming in Python for Beginners with Apache Spark 3 - Spark Transformations and Actions

The video tutorial explains the basics of Spark data processing, focusing on dataframes, their immutability, and how transformations and actions work. It covers the concept of narrow and wide dependency transformations, the importance of lazy evaluation, and how actions trigger execution. The tutorial also highlights the creation of a DAG of operations and the role of the Spark driver in optimizing execution plans.

What is a Spark Dataframe and how does it differ from a traditional data structure?

Explain the concept of immutability in Spark Dataframes.

What are transformations in Spark, and how do they work?

Explain the builder pattern used in creating a Spark session.

What is the significance of creating a Directed Acyclic Graph (DAG) in Spark?

Describe the difference between narrow and wide dependency transformations.

How does Spark handle shuffle and sort operations during wide dependency transformations?

What is lazy evaluation in Spark, and why is it important?

How can you optimize Spark transformations and actions for better performance?

What are actions in Spark, and how do they differ from transformations?