Data Engineering with Python: Build ETL Pipelines and Data Workflows from Scratch

By Miguel Farmer

Turn Raw Data into Reliable Insights-One Pipeline at a Time.
In a world where data is the new oil, being able to move, clean, transform, and store data efficiently is a game-changing skill. Data Engineering with Python gives you the tools and know-how to build end-to-end ETL pipelines and robust data workflows-from scratch.

This book is built for developers, analysts, and aspiring data engineers who want a clear, hands-on guide to real-world data engineering. You'll master how to extract data from APIs and databases, clean and structure it using Python, and load it into data warehouses for downstream analysis. With step-by-step walkthroughs, best practices, and scalable architecture patterns, you'll go beyond the theory and start building production-grade systems.

Whether you're working with batch or streaming data, local files or cloud services-this book will equip you with the Python-first approach to build pipelines that are scalable, maintainable, and ready for the modern data stack.

✅ What You'll Learn:

ETL fundamentals and how to build pipelines from zero

Using Pandas, SQLAlchemy, and Airflow for real-world workflows

Connecting to APIs, CSVs, SQL, NoSQL, and cloud storage

Data validation, logging, and error handling for clean pipelines

Introduction to orchestration, scheduling, and automation

Best practices for modular, testable pipeline code

Perfect For:

Aspiring data engineers and developers

Data analysts looking to automate and scale workflows

Backend engineers working with data-heavy applications

Anyone transitioning into data engineering roles

• Format: Paperback
• Language: English
• ISBN: B0FJMFXR5Y
• ISBN13: 9798293649228
• Release Date: July 2025
• Publisher: Independently Published
• Length: 272 Pages
• Weight: 0.81 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"