Apache Spark 3 for Data Engineering and Analytics with Python - DAG Visualisation

The video tutorial revisits the Spark Web UI, explaining its components and the concept of directed acyclic graphs (DAGs) to illustrate how the Spark engine operates. It covers the execution of stages, the role of transformations and actions in Spark's lazy execution model, and the visualization of DAGs. The tutorial also delves into the code generation process that enhances execution performance by converting Spark SQL code into Java bytecode.

What is the primary purpose of the Spark Web UI?

• To visualize and monitor Spark applications

• To manage Python libraries

• To compile Java bytecode

• To write Spark code

In the Spark Web UI, what does each stage represent?

• A complete Spark application

• A unit of work

• A single task

• A Python script

What is the role of PY4J in Spark?

• To compile Scala code

• To manage Spark clusters

• To convert Spark SQL to Java bytecode

• To provide a Python API for Spark

What triggers the execution of transformations in Spark?

• The loading of a data frame

• The call to an action

• The completion of a job

• The start of a Spark session

Which of the following is an example of a Spark action?

• Select

• Filter

• Show

• Order By

What does the DAG visualization in Spark help you understand?

• The memory usage of Spark

• The Python code structure

• The sequence of executed tasks

• The network configuration

What is the purpose of whole stage codegen in Spark?

• To improve execution performance by generating Java bytecode

• To compile Python scripts

• To manage Spark jobs

• To visualize data frames