Apache Spark 3 for Data Engineering and Analytics with Python - Preparing the Project Folder

This video tutorial guides viewers through setting up a project folder for a Spark DataFrame project. It covers navigating directories, creating a local Python environment, and installing essential packages like Pyspark, Pandas, and Seaborn. The tutorial also demonstrates how to launch Jupyter Lab for further development. By the end, viewers will have a ready-to-use environment for learning and building Spark projects.

What is the first step in setting up the project folder?

• Install Python libraries

• Activate the Python environment

• Navigate to the last Spark test folder

• Create a new directory called Arc DF

Which command is used to create a new directory for datasets?

• cd data

• rm data

• mkdir data

• ls data

What confirms the successful creation of the local Python environment?

• Presence of global Python libraries

• VENV in brackets on the command prompt

• Error message in the terminal

• Automatic installation of packages

Which package is installed first to assist with a modern way of installing other packages?

• Jupyter Lab

• Will

• Seaborn

• Pandas

What is the purpose of installing Seaborn in the project?

• Data collection

• Data visualization

• Data cleaning

• Data storage

Which command is used to launch Jupyter Lab from the command line?

• jupyter notebook

• jupyter space lab

• jupyter lab

• jupyter start

What is the main focus of the next lessons after setting up the environment?

• Learning Python basics

• Installing more libraries

• Exploring Spark structured APIs

• Understanding data storage