Apache Spark: Design & Execute ETL Pipelines Hands-On
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Apache Spark: Design & Execute ETL Pipelines Hands-On
This course is part of Spark and Python for Big Data with PySpark Specialization
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What you'll learn
Install and configure PySpark, Hadoop, and MySQL for ETL workflows.
Build Spark applications for full and incremental data loads via JDBC.
Apply transformations, handle deployment issues, and optimize ETL pipelines.
Skills you'll gain
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There are 2 modules in this course
This hands-on course equips learners with the skills to design, build, and manage end-to-end ETL (Extract, Transform, Load) workflows using Apache Spark in a real-world data engineering context. Structured into two comprehensive modules, the course begins with foundational setup, guiding learners through the installation of essential components such as PySpark, Hadoop, and MySQL. Participants will learn how to configure their environment, organize project structures, and explore source datasets effectively.
As the course progresses, learners will develop Spark applications to perform full and incremental data loads using JDBC integration with MySQL. Through practical examples, they will apply transformation logic using Spark SQL, filter data based on business rules, and handle common pitfalls such as type mismatches and folder structure issues during Spark deployment. By the end of the course, learners will be able to construct, execute, and optimize Spark-based ETL pipelines that are scalable and production-ready, empowering them to contribute effectively in real-world data engineering roles.
This module introduces learners to the fundamentals of building an ETL framework using Apache Spark. It begins by providing an overview of the Spark ecosystem and its advantages in big data processing. Learners will be guided through the installation and configuration of essential software packages, setting up the development environment, and understanding the structure of a Spark-based ETL project. The module also covers how to work with real-world datasets and prepare configuration files for database interactions—laying a strong groundwork for scalable data processing workflows.
What's included
5 videos3 assignments
5 videos•Total 52 minutes
- Introduction to Project•14 minutes
- Installation of Packages•7 minutes
- Installation of Packages Continue•8 minutes
- Setting up Project Structure•10 minutes
- Exploring Dataset•12 minutes
3 assignments•Total 60 minutes
- Graded Quiz - Setting Up the Foundation•30 minutes
- Getting Started with the ETL Project•15 minutes
- Building the Project Structure and Understanding Data•15 minutes
This module guides learners through the practical implementation of Extract, Transform, and Load (ETL) processes using Apache Spark. Learners will explore full data loads into MySQL, apply transformation logic using Spark SQL, and handle incremental loading scenarios by tracking and managing new records. The lessons include error handling, filtering strategies, data type compatibility, and database integration using JDBC—all within a hands-on PySpark environment. This module reinforces applied knowledge of Spark for real-world data engineering tasks.
What's included
6 videos3 assignments
6 videos•Total 47 minutes
- Entire Load and Transformations Part 1•7 minutes
- Entire Load and Transformations Part 2•7 minutes
- Entire Load and Transformations Part 3•7 minutes
- Entire Load and Transformations Part 4•9 minutes
- Incremental Load•7 minutes
- Incremental Load Continue•10 minutes
3 assignments•Total 60 minutes
- Graded Quiz – Building ETL Workflows in Apache Spark•30 minutes
- Complete Load and Transformations•15 minutes
- Handling Incremental Loads•15 minutes
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- 5 stars
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Reviewed on Jan 19, 2026
Learners feel they actually build powerful pipelines — from raw ingestion to analytics-ready outputs, not just toy examples.
Reviewed on Jan 31, 2026
Great mix of theory and hands-on labs. I now feel comfortable using DataFrames, Spark SQL, and basic optimization techniques.
Reviewed on Jan 5, 2026
I liked how this course didn’t just talk about Spark, but actually showed me how to build and run ETL pipelines — that’s rare in short courses.
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