Summary  
This chapter covers how to use window functions in Spark SQL to perform computations across sets of rows related to the current row, including partitioning, ordering, and defining frame boundaries for functions like row_number, rank, lag/lead, and aggregate functions.

General domain of usage  
Data analytics

Master scalable data processing and analysis using PySpark, focusing on real-world e-commerce datasets. Learn to query, clean, transform, and prepare data for analytics and machine learning using Spark SQL and DataFrame APIs.

Comprehensive coverage of PySpark data processing techniques for e-commerce data, from querying to cleaning, feature engineering, and performance optimization.

Window Functions in Spark SQL