Summary  
This chapter explains how to read, write, and transform data using CSV, JSON, and Parquet formats, demonstrating serialization and deserialization of structured datasets and performance trade-offs of columnar storage.

General domain of usage  
Data engineering

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 Data Formats: CSV, JSON, Parquet