Summary  
This chapter covers how to manipulate, parse, format, and extract information from date and time data in PySpark using built-in functions like to_timestamp, date_format, date_add, date_diff, and window operations.

General domain of usage  
Time series data analysis

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.

Working with Dates and Times in PySpark