Summary  
This chapter covers functional transformations on collections—specifically demonstrating how to use map to apply a function to each element, filter to retain elements based on a predicate, and flatMap to both map and flatten nested results.

General domain of usage  
Data processing pipelines

Learn the essentials of PySpark for big data analysis using real-world flights data. Explore Spark architecture, RDDs, DataFrames, and practical data processing techniques.

Core concepts and hands-on practice with PySpark using flights data.

RDD Transformations: map(), filter(), flatMap()