Modern data engineering is built on distributed data processing, and PySpark has become one of the most important technologies for handling large-scale data pipelines in production environments. This course is designed to take you from PySpark fundamentals to advanced data engineering concepts using practical, real-world examples and a complete end-to-end retail analytics project.
Unlike basic Spark tutorials that only teach syntax, this course focuses on how real data engineers work with Apache Spark in production systems. You will understand not only how to write PySpark code, but also why Spark behaves the way it does internally.
The course begins with Spark foundations and gradually moves into distributed processing, Spark architecture, transformations, schema management, optimization techniques, analytics functions, data storage strategies, streaming, and production-style engineering workflows. You will also build a complete retail analytics data engineering pipeline using PySpark and Docker, where you will process over 1 million records using Medallion Architecture (Bronze, Silver, and Gold layers).
