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

This video tutorial explains the concept of window aggregates, focusing on computing running totals for each country on a week-by-week basis. It covers the importance of partitioning data by country and ordering it by week number. The tutorial demonstrates how to use sliding windows to calculate running totals and provides a step-by-step guide to implementing this in code. The video concludes with examples of different aggregation functions and analytical functions that can be used with window aggregates.

What is the primary goal of using window aggregates in this context?

• To compute the total sales for each product.

• To calculate running totals for each country on a weekly basis.

• To find the average sales per week.

• To determine the highest sales week for each country.

Why is it important to partition the data by country when using window aggregates?

• To simplify the data structure.

• To ensure each country's data is processed independently.

• To increase the speed of data processing.

• To reduce the size of the data frame.

What is the significance of ordering each partition by week number?

• To group data by month.

• To compute running totals in the correct sequence.

• To filter out incomplete data.

• To ensure the data is sorted alphabetically.

How does the sliding window mechanism work in computing running totals?

• It only considers the last record in the partition.

• It calculates totals by considering only the first record.

• It extends the window by one record at a time to compute cumulative sums.

• It averages the values of all records.

What is the purpose of using 'ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW' in the window definition?

• To exclude the first record from the calculation.

• To limit the window to the last three records.

• To include all records from the start up to the current record.

• To only consider the current record.

Which function is used to add a new column for running totals in the data frame?

• appendColumn

• addColumn

• withColumn

• insertColumn

What other aggregation functions can be used over the window besides sum?

• Only DENSE RANK.

• Only average and mean.

• Average, mean, and other analytical functions like DENSE RANK.

• Only sum can be used.