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 main requirement discussed in the video regarding window aggregates?

How does the running total for each country restart according to the video?

Explain how the running total is calculated using a sliding window of records.

What are the three critical steps mentioned for defining a window in window aggregates?

What does 'ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW' signify in the context of window aggregates?

How can you modify the start of the window in the running total calculation?

What types of aggregation functions can be used with window aggregates as mentioned in the video?