1. Snowflake Data Engineering
Author: Maja Ferle
Released on January 28, 2025, “Snowflake Data Engineering” is a must-read for data professionals looking to delve into modern data architectures. This book provides an insightful overview of Snowflake’s capabilities, emphasizing how it revolutionizes data engineering through its agile, cloud-based platform. Maja Ferle walks readers through practical implementations, ensuring that they not only understand the concepts but can also apply them effectively in their careers. With a perfect blend of theory and practice, this guide is essential for those aiming to excel in the data-driven landscape.
2. Hands-On Data Engineering with R, Python and PostgreSQL
Authors: Michel Ballings, Dirk Van den Poel
Expected publication on December 14, 2024, this comprehensive guide implements the best practices of data engineering using R, Python, and PostgreSQL. Michel Ballings and Dirk Van den Poel merge hands-on projects with theoretical insights to help readers build robust data pipelines. By leveraging these popular programming languages, the book is ideal for both beginners and seasoned engineers who want to harness the power of data in analytical processes. It opens doors to a deeper understanding of data workflows, making it a crucial addition to any data engineer’s library.
3. Data Engineering with Databricks Cookbook
Author: Pulkit Chadha
Set to be released on May 31, 2024, the “Data Engineering with Databricks Cookbook” equips readers with practical recipes designed to harness the power of Apache Spark, Databricks, and Delta Lake. Pulkit Chadha expertly curates solutions to common data engineering problems, guiding readers step-by-step through the setup, execution, and optimization of data processes. This cookbook is perfect for practitioners who want immediate results, enhancing their data manipulation capabilities in real-world scenarios. It’s a treasure trove of knowledge for every aspiring data engineer.
4. Data Engineering with dbt
Author: Roberto Zagni
Released on June 30, 2023, “Data Engineering with dbt” introduces readers to the tools necessary for building cloud-native data platforms using SQL. Roberto Zagni delves into best practices for using dbt (data build tool) to transform raw data into actionable insights while ensuring a reliable architecture. As organizations increasingly adopt cloud solutions, understanding dbt’s capabilities is essential, and this guide makes the learning process both enjoyable and insightful, allowing a seamless transition into data engineering roles.
5. The Data Engineering Handbook
Author: Joe Trite
Scheduled for release on October 17, 2024, “The Data Engineering Handbook” captures the essence of what it means to be a data engineer. Joe Trite shares insights from his experience in the field, showcasing how data engineers turn chaotic data into valuable insights. This book covers everything from foundational concepts to advanced techniques, making it accessible to newcomers while still providing depth for seasoned professionals. It’s a motivational and educational read that inspires engineers to push their limits.
6. Data-Driven Science and Engineering
Authors: Steven L. Brunton, J. Nathan Kutz
With a publication date of July 28, 2022, “Data-Driven Science and Engineering” takes a unique approach, integrating machine learning with dynamical systems and control theory. Steven L. Brunton and J. Nathan Kutz present complex topics in an engaging manner, promoting a deep understanding of modern data applications. This book is perfect for anyone interested in the intersection of data engineering and scientific discovery, as it broadens perspectives on data application, making it pivotal for future innovations in engineering.
7. Data Engineering with Google Cloud Platform
Author: Adi Wijaya
Expected to hit the shelves on April 30, 2024, this guide offers a compelling roadmap for building scalable data platforms using Google Cloud. Adi Wijaya equips readers with the necessary tools to navigate Google’s ecosystem, including BigQuery and Dataflow. This book is tailored for both newcomers and veterans eager to enhance their cloud data engineering skills, ensuring that they can create efficient, scalable architecture for data management. It’s an essential resource for anyone looking to boost their career in the cloud realm.
8. Official Google Cloud Certified Professional Data Engineer Study Guide
Author: Dan Sullivan
This 2020 publication is the ultimate study guide for those aspiring to become Google Cloud Certified Professional Data Engineers. Dan Sullivan provides a comprehensive overview of the exam requirements while encompassing essential concepts such as data modeling, ETL processes, and machine learning. This guide also provides valuable hands-on exercises that empower readers to deepen their understanding – a vital step for anyone looking to validate their skills in a cloud-based environment. A must-have for serious professionals.
9. Data Engineering with Python Cookbook
Author: Adithyan Ramanujakootam
Releasing on October 3, 2023, this cookbook emphasizes building efficient data pipelines using the Modern Cloud Data Stack. Adithyan Ramanujakootam presents a series of recipes that guide readers through the practical steps of data handling, making it invaluable for data professionals. The book covers diverse techniques and tools, ensuring that readers have a toolkit for any data engineering challenge they face. It’s a perfect blend of practicality and in-depth knowledge that will aid anyone’s journey through the data landscape.
10. Software Engineering for Data Scientists
Author: Catherine Nelson
Set to be released on May 21, 2024, “Software Engineering for Data Scientists” is essential for anyone looking to transition from data analysis to engineering. Catherine Nelson comprehensively covers how to build scalable systems out of data science projects, emphasizing best coding practices, version control, and deployment techniques. Readers will gain invaluable insights on how to adapt data science work into production-ready applications, making this an indispensable resource for imbibing engineering practices into data science.
