PySpark and AWS: Master Big Data with PySpark and AWS - Solution (Group By)

This video tutorial covers data processing using Spark DataFrames. It begins with reading data from a CSV file and creating a DataFrame. The tutorial then demonstrates how to group data by course to count student enrollments, followed by grouping by gender to display enrollments. It further explains calculating the total marks achieved by each gender in each course using sum aggregation. Finally, it covers displaying minimum, maximum, and average marks achieved in each course by age group, emphasizing the importance of the group by feature in Spark.

What is the first step in analyzing student data using Spark DataFrame?

• Creating a new database

• Reading data from a CSV file

• Writing data to a JSON file

• Deleting existing data

Which function is used to group data by course to count enrollments?

• order by

• group by

• select

• filter

How can you extend the group by operation to count enrollments by gender?

• Add a filter for gender

• Include gender in the group by clause

• Change the CSV file

• Use a different DataFrame

What aggregation function is used to calculate the total marks achieved by each gender?

• sum

• min

• average

• count

Which aggregation functions are used to calculate marks statistics by age group?

• count, max, min

• average, sum, count

• min, max, average

• sum, count, average

What is the purpose of using group by with age group in the final section?

• To delete data for certain age groups

• To calculate statistics for each age group

• To sort data by age

• To filter data by age

What is the main focus of the video tutorial?

• Machine learning algorithms

• Database management

• Data analysis using Spark DataFrame

• Creating visualizations