Summary  
This chapter covers methods for detecting, validating, and cleaning inconsistent or malformed data by using error-handling routines, type checks, default values, and format normalization to ensure robust data processing.

General domain of usage  
Data preprocessing

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.

Handling Inconsistent and Malformed Data