Data Engineering Fundamentals

In today’s data-driven world, mastering data engineering is crucial for anyone looking to build robust data pipelines and extract valuable insights. This book simplifies complex concepts and provides a clear pathway to understanding the core principles that power modern data solutions. It bridges the gap between raw data and actionable intelligence, making data engineering accessible to everyone.

This book walks you through the entire data engineering lifecycle. Starting with foundational concepts and data ingestion from diverse sources, you will learn how to build efficient data lakes and warehouses. You will learn data transformation using tools like Apache Spark and the orchestration of data workflows with platforms like Airflow and Argo Workflow. Crucial aspects of data quality, governance, scalability, and performance monitoring are thoroughly covered, ensuring you understand how to maintain reliable and efficient data systems. Real-world use cases across industries like e-commerce, finance, and government illustrate practical applications, while a final section explores emerging trends such as AI integration and cloud advancements.

By the end of this book, you will have a solid foundation in data engineering, along with practical skills to help enhance your career. You will be equipped to design, build, and maintain data pipelines, transforming raw data into meaningful insights.

WHAT YOU WILL LEARN
● Understand data engineering base concepts and build scalable solutions.
● Master data storage, ingestion, and transformation.
● Orchestrates data workflows and automates pipelines for efficiency.
● Ensure data quality, governance, and security compliance.
● Monitor, optimize, and scale data solutions effectively.
● Explore real-world use cases and future data trends.

WHO THIS BOOK IS FOR
This book is for aspiring data engineers, analysts, and developers seeking a foundational understanding of data engineering. Whether you are a beginner or looking to deepen your expertise, this book provides you with the knowledge and tools to succeed in today’s data engineering challenges.

1. Understanding Data Engineering
2. Data Ingestion and Acquisition
3. Data Storage and Management
4. Data Transformation and Processing
5. Data Orchestration and Workflows
6. Data Governance Principles
7. Scaling Data Solutions
8. Monitoring and Performance
9. Real-world Data Engineering Use Cases
10. Future Trends in Data Engineering

Zhaolong Liu is a seasoned Data Platform Engineer and Lead Data Engineer with extensive experience in data engineering, site reliability, and cloud infrastructure. With expertise in Kubernetes, Spark, and cloud technologies, he leads complex projects, including real-time data processing, CI/CD automation, and event-driven data pipelines.

During his studies, he worked as a programming assistant at his university, gaining early experience in software development. He later contributed to large-scale data engineering and site reliability projects at FlixBus and Miro. He currently works at AVIV Group in Berlin, Germany.