Apache Spark 3 for Data Engineering and Analytics with Python - Challenge Part 1 – Brief

This video tutorial guides viewers through a data preparation challenge using Spark in a Jupyter Notebook. It covers importing libraries, creating a Spark session, setting up a schema, reading CSV files, and displaying data. The tutorial emphasizes the importance of cleaning raw data and encourages viewers to practice the tasks independently, referring to attached documentation for guidance.

What is the first task you need to perform in the challenge?

• Create a new Spark session

• Import the necessary Spark libraries

• Read a CSV file

• Set up a data folder

Why are all data types initially set to strings in the schema?

• Because the data is already clean

• To allow for data cleaning before assigning correct types

• To simplify the schema creation process

• To ensure compatibility with all data formats

What is the purpose of creating a Spark session in this challenge?

• To manage data storage

• To perform data analytics

• To import CSV files

• To visualize data

What should you do after downloading the sales data zip file?

• Read the files directly from the zip

• Extract the files into a new data folder

• Convert the files to JSON format

• Upload the files to a cloud storage

What is the final task mentioned in the video?

• Submit the completed notebook

• Import additional libraries

• Show the first 10 records and print schema details

• Create a new schema