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

The video tutorial guides viewers through the process of calculating average scores from a dataset using Spark. It begins with a problem statement, followed by data preparation and environment setup. The tutorial then explains how to implement a mapper function to create key-value pairs, followed by using reduce by key for aggregation. Finally, it demonstrates calculating averages for each month and concludes with a summary of the solution.

What is the primary goal of the problem statement discussed in the video?

• To calculate the total score for each city

• To determine the highest score in a month

• To calculate the average score for each month

• To find the month with the most ratings

Why is it unnecessary to consider city data in the RDD operations?

• City data is irrelevant to the average score calculation

• City data is too complex to process

• City data is not available in the dataset

• City data is already included in the ratings

What is the purpose of creating a key-value pair in the RDD?

• To organize data by city and month

• To store city names and their ratings

• To calculate the total number of ratings

• To map month names to their ratings

What does the mapper function do in the context of this problem?

• It calculates the total score for each month

• It filters out unnecessary data

• It converts data into a key-value format

• It sorts the data by month

How does the reduceByKey operation help in this solution?

• It filters out duplicate ratings

• It groups and sums ratings by month

• It sorts the ratings in ascending order

• It calculates the average rating directly

What is the final output of the reduceByKey operation?

• A list of cities with their total ratings

• A list of months with their average ratings

• A list of months with their cumulative ratings and counts

• A sorted list of ratings

What is the next step after obtaining cumulative ratings and counts?

• To sort the ratings by month

• To calculate the average rating for each month

• To filter out months with low ratings

• To display the highest rating

What is the significance of converting data to a float in this context?

• To reduce memory usage

• To improve the speed of calculations

• To simplify the data processing

• To ensure accurate division for average calculation

What is the final result of the entire RDD operation process?

• A list of cities with their average ratings

• A list of ratings sorted by value

• A list of months with their average ratings

• A list of months with their total ratings

What is the main takeaway from the video regarding RDD operations?

• RDDs are only useful for sorting data

• RDDs simplify the process of data aggregation

• RDDs are not suitable for average calculations

• RDDs are primarily used for filtering data