PySpark and AWS: Master Big Data with PySpark and AWS - Average Marks per Course

This video tutorial explains how to calculate average marks per course using transformations in data processing. It covers breaking down the process into steps, using reduce by key to group data, and applying map values for key-value manipulation. The tutorial aims to enhance understanding of data transformations and key-value pair operations.

What is the first step in calculating average marks per course?

• Calculating the average directly

• Counting the enrollments

• Using map values

• Summing the scores

Why is it important to use keys and tuples in data transformation?

• To avoid using lambda functions

• To group and reduce data effectively

• To ensure data is stored in a database

• To make the code run faster

What is the purpose of creating a tuple in the transformation process?

• To store data in a database

• To group data by a specific key

• To ensure data is encrypted

• To convert data into a string

What does the 'reduce by key' function do in the context of this video?

• It sorts the data alphabetically

• It sums values based on a common key

• It filters out unnecessary data

• It multiplies all values by a constant

In the context of this video, what does a lambda function typically do?

• It creates a new database

• It deletes unnecessary data

• It defines a small anonymous function

• It sorts data in ascending order

How does the 'map' function help in calculating averages?

• By transforming data elements

• By filtering out zero values

• By directly providing the average

• By storing data in a new format

What mistake was corrected when using the 'map' function for averages?

• Forgetting to import a library

• Incorrectly referencing tuple elements

• Using a deprecated function

• Using the wrong data type

What is the main advantage of using the 'map values' function?

• It focuses on values, simplifying transformations

• It changes the data type of keys

• It increases the speed of data processing

• It automatically calculates averages

How does 'map values' differ from 'map' in terms of data handling?

• It is slower in execution

• It focuses solely on values

• It only processes keys

• It requires more memory

What is the final output when using 'map values' correctly?

• A sorted list of data

• A single average value

• A list of transformed values

• A list of keys