Build practical data engineering skills by learning how to design, develop, and execute end-to-end ETL (Extract, Transform, Load) pipelines using Apache Spark. In this hands-on course, you will begin by setting up a Spark development environment, installing and configuring PySpark, Hadoop, and MySQL, organizing ETL project structures, and exploring real-world datasets.

As you progress, you will implement complete and incremental ETL workflows using Apache Spark. You'll integrate Spark with MySQL through JDBC, apply data transformation logic with Spark SQL, perform business-rule filtering, and address common issues such as data type compatibility and project structure challenges. Through guided, practical exercises, you'll gain experience building scalable ETL workflows in a PySpark environment. This course is designed for aspiring data engineers, big data practitioners, and learners who want practical experience with Apache Spark-based ETL development. By the end of the course, you will be able to construct, execute, and optimize Spark ETL pipelines, implement full and incremental data loading strategies, and integrate Spark applications with relational databases using JDBC for real-world data engineering workflows.