A Comprehensive Guide to CSV Files vs. Parquet Files in PySpark
When working with large-scale data processing in PySpark, understanding the differences between data formats like CSV and Parquet is essential for efficient data storage, query performance, and scalability. In this guide, we’ll compare CSV and Parquet files, explore their strengths and weaknesses, and provide examples of how to work with both formats in PySpark. 1. What is a CSV File? A CSV (Comma-Separated Values) file is a simple text-based format where each row represents a record, and columns are separated by commas (or other delimiters like tabs or semicolons). CSV files are widely used due to their simplicity and compatibility with many systems. Characteristics of CSV: Human-readable: CSV files are plain text, making them easy to open and read in any text editor. No schema: CSV files don’t store metadata (like data types). This can make it harder to work with complex data structures. Slower performance: Since CSV is not a binary format, reading and writing l...