Apache Spark 3 for Data Engineering and Analytics with Python - Spark Application and Session

This video tutorial guides viewers through setting up a project directory, launching Jupyter Notebook, and creating a Spark session. It covers importing necessary libraries and functions for data aggregation and explains the core components of a Spark application, including the Spark driver program and Spark SQL. The tutorial concludes with a recap of key concepts and encourages further exploration of Spark classes and functions.

What is the first step in setting up the project directory for the Spark program?

• Create a new directory and navigate to it

• Import necessary libraries

• Download the sales data CSV file

• Start the Jupyter Notebook

Which command is used to start a Jupyter Notebook?

• jupyter start

• jupyter notebook

• jupyter run

• jupyter open

What is the purpose of importing the 'count' function from the Spark SQL library?

• To navigate directories

• To start the Jupyter Notebook

• To aggregate data by counting occurrences

• To create a new Spark session

What is the main advantage of using Spark SQL over traditional RDBMS?

• It offers easier-to-follow functions

• It is faster to set up

• It is more complex

• It requires less memory

What is the role of the Spark session in a Spark application?

• To manage the user interface

• To facilitate communication between the application and executors

• To create directories

• To store data files

How many Spark sessions can exist per JVM?

• One

• Multiple

• Unlimited

• None

What does JVM stand for in the context of Spark?

• Java Virtual Machine

• Java Variable Module

• Java Version Manager

• Java Visual Model