
Fundamentals of Data Engineering
by Joe Reis, Matt Housley (2022)
Like '97 Things Every Data Engineer Should Know', this book offers foundational knowledge for data engineers.

by Tobias Macey (2021)
With this in-depth book, data engineers will learn powerful, real-world best practices for managing data big and small. Contributors from companies including Google, Microsoft, IBM, Facebook, Databricks, and GitHub share their experiences and lessons learned for cleaning, prepping, wrangling, storing, processing, and ingesting data. Current and aspiring data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will get targeted advice for overcoming a variety of specific challenges from engineers at major companies. Projects include: Building pipelines Stream processing Data privacy and security Data governance and lineage Data storage and architecture Ecosystem of modern tools Data team makeup and culture Career advice
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by Joe Reis, Matt Housley (2022)
Like '97 Things Every Data Engineer Should Know', this book offers foundational knowledge for data engineers.

by Martin Kleppmann (2015)
Similar to '97 Things Every Data Engineer Should Know', this delves into the core principles of data systems.

by Paul Crickard (2020)
Echoing '97 Things Every Data Engineer Should Know', this book provides practical Python-based data engineering skills.

by Ralph Kimball, Margy Ross (1996)
Following the spirit of '97 Things Every Data Engineer Should Know', this offers deep insights into data warehousing.

by Alex Petrov (2019)
Like '97 Things Every Data Engineer Should Know', this book dives deep into the mechanics of data systems.
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