Apache Spark 3 for Data Engineering and Analytics with Python - Challenge Part 3 - Q4 Products Bought Together

The video tutorial addresses a complex question about identifying products frequently sold together in New York. It guides through setting up a data analysis environment using PySpark, preparing and filtering data, and aggregating it to create product lists per order. The analysis reveals that the Google phone and USB C charging cable are often bought together. The session concludes with a summary of findings.

What is the main objective of the analysis discussed in the video?

• To calculate the total sales revenue in New York

• To determine the least popular products in New York

• To identify products frequently sold together in New York

• To find the most expensive products sold in New York

Which library is imported to help with data aggregation?

• NumPy

• Pandas

• PySpark

• Matplotlib

What function is used to create a list of products for each order?

• orderBy

• filter

• collect_list

• groupBy

What mistake did the narrator identify during the data processing?

• Missing product names

• Duplicate records

• Incorrect order IDs

• Missing state information

What is the purpose of the 'size' function in the analysis?

• To sort the products by price

• To filter out single-product orders

• To calculate the total sales

• To determine the number of products in each list

Which product combination was found to be frequently bought together?

• Google phone and USB C charging cable

• Headphones and charger

• Laptop and mouse

• Tablet and stylus

What is the final step in the analysis process?

• Filtering the data by state

• Counting the frequency of product combinations

• Restarting the notebook

• Creating a new data frame