"Data Observability for Data Engineering" introduces you to the foundational concepts of observing and validating data pipeline health. With real-world projects and Python code examples, you'll gain hands-on experience in improving data quality and minimizing risks, enabling you to implement strategies that ensure accuracy and reliability in your data systems. What this Book will help me do Master data observability techniques to monitor and validate data pipelines effectively. Learn to collect and analyze meaningful metrics to gauge and improve data quality. Develop skills in Python programming specific to applying data concepts such as observable data state. Address scalability challenges using state-of-the-art observability frameworks and practices. Enhance your ability to manage and optimize data workflows ensuring seamless operation from start to end. Author(s) Authors Michele Pinto and Sammy El Khammal bring a wealth of experience in data engineering and observing scalable data systems. Pinto specializes in constructing robust analytics platforms while Khammal offers insights into integrating software observability into massive pipelines. Their collaborative writing style ensures readers find both practical advice and theoretical foundations. Who is it for? This book is geared toward data engineers, architects, and scientists who seek to confidently handle pipeline challenges. Whether you're addressing specific issues or wish to introduce proactive measures in your team, this guide meets the needs of those ready to leverage observability as a key practice.