A practical beginner-level guide to PySpark that goes beyond the basics, covering schema definition for safer CSV reading, data cleaning techniques (dropna, fillna, cast, dropDuplicates), joining DataFrames, using Parquet as a more efficient file format, and organizing transformations into a clear workflow. Also introduces the Spark UI for basic job monitoring. All examples run locally without a cluster.
Table of contents
Beyond the basics1. Taking the next step: reading data properly2. Understanding data transformations3. Cleaning data before it causes problems4. Joining datasets in PySpark without getting lost5. Reading & Writing data out in the “Spark way”: Parquet6. Thinking in PySpark workflows7. A gentle introduction to the Spark UISummary: You’re now ready for your first real PySpark project213 Impressions