Book: Data Engineering Design Patterns (DEDP)
Hey there 👋, this is the start of a book about Data Engineering Design Patterns.
About This Book
This book is different from usual books. It does not come finished. I will steadily release new chapters of the book, carefully listen to all your feedback, and integrate them to create a (hopefully) great book at the end of the day. Keep an eye on the changelog for the latest updates, or sign up for the below newsletter.
Motivation
I love writing. I love editing text. After writing for about 8+ years on my personal blog and even doing it professionally for a year, I love the challenge that my former boss confronted me to put my 20+ years of experience in data engineering into a book. And, as I love long formats, a book might just be the best format to bring it all together in one.
Over my 20+ year career, I've witnessed an endless cycle of emerging terms and technologies. This pattern led me to ponder: are we merely repackaging old ideas in new terminology?
That's when I found the term the intriguing concept: convergent evolution. This term, new to me at first, resonated deeply with my experiences in the data field, where often new terms are introduced every week. Over time, I learned that what seemed new was simply a repurposed concept from a decade ago, now labeled with a new name.
Occasionally, these were existing technologies, subtly enhanced. Yet, true innovation seemed rare. This is where the idea of convergent evolution becomes pivotal.
Convergent evolution is when the outcome of two distinct evolutions is the same. The most famous one is flying. Both a bird and a bee can fly but in different ways. The bird has developed feathers, and a bee has an exoskeleton, which was learned and developed to fly in a different evolution.
This pattern of convergent evolution in data engineering captivated me, inspiring this book. My goal is to dissect these evolutionary paths, uncovering their unique strengths and weaknesses. By doing so, I aim to identify universal design patterns applicable across the spectrum of data engineering. Join me as we explore the essence of convergent evolution, its relevance in our field, and how it can guide us through the Data Engineering Lifecycle, enriching our understanding and practice.
Disclaimer
This project is still a personal project, mostly available for free, solely made by me, Simon Späti. Therefore, I might be wrong here and there; please let me know when so. Be kind 🤗.
What You Will Learn by the End of This Book
- The history and state of the art of data engineering.
- We cover Convergent Evolution, common pattern among them and find best practices in the form of data engineering design patterns.
- Gain a comprehensive understanding beyond the hype to deeply understand definitions, history and core concepts of data engineering.
- Dive into categories of data engineering such as different approaches, architectures and data modeling.
- Helping you navigate the complex environments of todays data landscape and improving decision-making for better business outcomes.
- Learning how to avoid common pitfalls, relevant tools and technologies and the future of data engineering through explored patterns and best practices.
What You Can Expect
- Genuine knowledge and wisdom from my 20+ experiences in the field of business intelligence and data engineering.
- To acquire a solid understanding of Convergent Evolution and the practical explanations of crucial data engineering design patterns.
- Stay informed in the constantly evolving world of data engineering by understanding essential data engineering design patterns beyond the hype.
Why Is This Book for You?
Looking to stay ahead of the curve in the constantly evolving world of data engineering? Look no further than this new book. With a focus on crucial data engineering design patterns and how to navigate them, you will gain a comprehensive understanding of how to overcome common challenges and build well-thought-through data systems.
The primary audience for this book on data engineering and the open data stack are professionals and practitioners working in data engineering. This field includes data engineers, data architects, data scientists, and data analysts who are responsible for designing, building, and maintaining data pipelines and data platforms.
The book is designed for an intermediate-level audience who are familiar with the basics of data engineering and have some experience working with data technologies. The audience is assumed to have a basic understanding of SQL and programming and experience working with data tools such as databases, ETL/ELT, and data modeling. The book assumes that you have already read or are aware of the introductory books on data engineering such as The Fundamentals of Data Engineering and are looking to explore the future of data engineering best practices.
The book does not assume mastery of any specific skills or technologies but rather provides a comprehensive overview of the data engineering landscape, including carefully curated approaches to data engineering. The book aims to provide you with a practical guide to applying data engineering design patterns using open-source tools to navigate complex, reliable, and maintainable data architectures and platforms.
What sets this book apart is its unique approach to iterating a live book online and actively seeking feedback. Analyzing critical terms of convergent evolution and how they blend into applicable data engineering design patterns. This innovative approach provides you with the tools and techniques needed to stand out in the competitive data engineering industry. Whether you're a seasoned professional or just starting out in the field, this book is a must-read for anyone looking to improve the efficiency and effectiveness of their open data stack and achieve data-aware solutions.
About Data Engineering Design Patterns
The field of data engineering is constantly changing, making it difficult for professionals to stay up to date with the latest trends and technologies. This book offers a unique solution by introducing the concept of convergent evolution and explaining how it relates to crucial data engineering design patterns. With topics ranging from CEs such as materialized views (which are equivalent to OBT), data validation (or data contracts), OLAP cubes (or semantic layers, personalized APIs, BI dashboard), different flavors of dimensional modeling, and various data integration techniques like ETL, ELT, Reverse ETL, CDP, and Master Data Management, you will gain a comprehensive understanding how these leads into most important design patterns such as Open Data Platform, Dynamic-Querying, Declarative Orchestration, Asset-based Governance and many more.
Part One of the book introduces the field of data engineering with history, explaining the current state, and highlighting common challenges while explaining what are design patterns and why convergent evolution plays a big part in it. Part Two delves into mastering these design patterns, exploring various data patterns resulting in design patterns, e.g., for architectural and data modeling or software engineering approaches applied to data engineering. Part Three provides guidance on how to navigate these patterns in real-world contexts, providing best practices, open data architectures, and emerging technologies. More on the next introduction chapter.
By examining emerging technologies and offering insights into the design patterns to navigate common challenges, this book provides a valuable resource for data engineers looking to improve the efficiency and effectiveness of their work.
By learning and applying these design patterns, data engineers can reduce conceptual errors, build more robust data systems, and ultimately contribute to better decision-making and business outcomes. Whether you're a seasoned data engineer or just curious about the field, this book is a must-read for anyone looking to stay ahead of the curve in this constantly evolving industry.