Data Engineering with dbt: A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL

One of my favorite data engineering books.This book is 50% theory, 50% practical. So a lot of what you learn you can actually put into practice.This is a big book filled with great information much of it extending beyond dbt.I'm looking forward to his new book.It might be a bit too long for some, but I enjoyed the value this book is.

This book is a must-read for data engineers and anyone interested in building a cloud-based, pragmatic, and dependable data platform using SQL and dbt. Roberto Zagni, a seasoned leader in data architecture and software development, provides practical guidance and insights that are invaluable in today's data-driven world.The author's expertise shines through as he takes readers on a journey through the foundations of data engineering, including SQL essentials, setting up a dbt Cloud development environment, and data modeling techniques. The explanations are clear, concise, and accompanied by real-world examples, making complex concepts easy to grasp.What sets this book apart is its focus on the modern data stack and the use of dbt, dbt Cloud, and Snowflake or BigQuery. Roberto Zagni demonstrates how to leverage these powerful tools to automate data pipelines, collaborate effectively, and ensure data quality. The step-by-step instructions and practical exercises make it a hands-on learning experience.I particularly appreciate the emphasis on maintainable code and agile data engineering practices. The author's insights into version control, testing, and designing for maintainability are invaluable for building robust and scalable data platforms.Data professionals often need to gain knowledge of various modeling techniques like Vault, Mesh, Bill Inmon, and Kimball modeling. The author of this book covers these topics, and I appreciate that.One aspect that could be improved is the level of detail in certain sections. While the book provides a good overview, some readers might benefit from more in-depth explanations and examples, especially when it comes to complex queries and advanced data modeling scenarios.Additionally, although the book mentions the importance of data quality and testing, it would have been helpful to see more comprehensive coverage and practical examples of implementing these practices within the dbt framework.As a data engineer, I found "Data Engineering with dbt" to be an indispensable resource that has enhanced my understanding of data engineering best practices. Whether you're a beginner or an experienced professional, this book will equip you with the knowledge and tools to excel in the field.I highly recommend "Data Engineering with dbt" to anyone passionate about data and even data scientists eager to stay ahead in the rapidly evolving world of data. Grab your copy and embark on a journey to build efficient and reliable data platforms.

Has a great walk through of each subject for most people.

"Data Engineering with dbt" isn't just a book about dbt; it's so much more than that. It delves into the entire ecosystem of technologies surrounding dbt, covering best practices in modeling, writing maintainable code that aligns with software engineering principles applied to the data realm, data transformation techniques, and the application of data quality tests to ensure consistent data delivery to the business. Additionally, it provides an introduction to tools within the Data Modern Stack, such as Snowflake, one of the most popular and robust Data Warehouses of the moment, Git, GitHub, and even using dbt in either its cloud version or the open-source version on our local machines. The book ultimately brings all these components together in an automation process for deployment, execution, and documentation.In essence, this book offers a comprehensive exploration of the data lifecycle, focusing on the transformation stage. It provides a refresher on SQL fundamentals, covers modeling, and guides readers to build robust, high-quality models following best practices.Rating:Clarity and Explanation: 5/5Depth and Coverage: 5/5Practical Examples: 4.7/5Progression and Difficulty: Easy to read / Entry levelPrerequisite Knowledge:- Basic SQL skills

Beginning with the basics of SQL for data transformation, the book swiftly progresses to setting up the dbt Cloud development environment. Through practical examples and step-by-step instructions, readers learn data modeling techniques, understand the changing role of analytics engineering, and harness dbt to transform and test data accurately. The book emphasizes the importance of maintainable code, working with dimensional data, ensuring consistency and reliability, and implementing agile development practices.With a focus on collaboration, automation, and enhancing software quality, the book equips readers to build data platforms that are agile, scalable, and well-documented. It also provides patterns for frequent use cases, guiding readers through common challenges encountered in data engineering projects. Whether readers are newcomers or experienced professionals, this book offers primers on data-related subjects to help them get started and excel in their roles."Data Engineering with dbt: A Practical Guide to Building a Cloud-Based, Pragmatic, and Dependable Data Platform with SQL" is a must-have guide for those seeking to build efficient and reliable data platforms. The book's clear explanations, practical examples, and emphasis on simplicity and maintainability make it accessible and valuable for individuals and teams alike. With its focus on leveraging dbt and SQL, this book empowers readers to become proficient in data engineering and drive successful data-driven initiatives.