Spark Programming in Python for Beginners with Apache Spark 3 - Grouping Aggregations

This video tutorial demonstrates how to set up a Spark session and load raw data into a DataFrame. It covers data preparation steps, including converting string dates to date fields and filtering data. The tutorial explains how to group data by country and week number, and perform aggregations using the AG method. It concludes with saving the results as a parquet file and a brief mention of colise, which will be covered in a future video.

What is the first step in preparing the data frame for grouping by week number?

• Add a new column for the country

• Filter the data for the year 2010

• Sort the data frame by invoice number

• Convert the invoice date to a date type

Which function is used to extract the week number from the invoice date?

• TO_DATE

• WEEK_OF_YEAR

• EXTRACT_WEEK

• DATE_PART

What is the purpose of using the AG method in this context?

• To group data by country

• To perform aggregations like count distinct and sum

• To convert string fields to date type

• To filter data by year

What file format is used to save the aggregated data frame?

• Parquet

• CSV

• JSON

• XML

What is the purpose of setting the mode to overwrite when saving the data frame?

• To append new data to the existing file

• To ensure the data frame is saved in a new directory

• To replace any existing file with the new data

• To save the data frame in multiple formats