Introduction 

Feel stuck somewhere at a bad code? Itching to try something new and different? want to learn more about your favorite subject? Tutorials can only get you so far, what you need to have are some Data Engineer Books to get you started. Books tend to cover everything from the basics to expert level problems and solutions. 

Here is my list of 10 Best Data Engineering Books

HADOOP: THE DEFINITIVE GUIDE

By Tom White (4th edition) is a book about Apache Hadoop and is a tool to help you unlock the power of your data. It teaches you to manage your large datasets, to build and maintain reliable, scalable, distributed systems with Hadoop. This is one of the basic data engineer books out there.

LEARNING SPARK

By Holden Karau is a valuable reference guide for an effective data processing framework in the industry these days. Data is generated in unimaginable volumes every second. Understanding and learning how to use this amount of data is made helpful through this genius book. This book does not limit itself to beginners and is helpful to students, researchers and professionals alike who love divulging in the vast world of Big Data application.

CLEAN CODE 

By Robert Cecil Martin is a highly rated and recommended book to start off with. A must have for data scientists and also software engineers who work mostly with codes. The name says it all, this book is essential if you want to start from ground up and learn more about clean and efficient coding. Even bad code can function. A clean code is a code that is easily understood by all and it’s very important while working with a team of developers. 

DEEP LEARNING

By Ian Goodfellow, Yoshua Bengio, and Aaron Courville is written by the experts in the field of data science. An introduction to an extensive list of topics in deep learning, covering mathematical and conceptual background, popular and latest techniques along with research approaches. A type of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. 

LEARN SQL THE HARD WAY

By Zed A. Shaw is a beginner’s web, desktop, or mobile applications building bible. Even if you’ve never made an app before, or have no database, programming, or SQL knowledge this book will surely be able to teach you almost everything you need to know about SQL application and guide you through your journey with ease. 

40 ALGORITHMS EVERY PROGRAMMER SHOULD KNOW 

By Imran Ahmad helps you by teaching numerous algorithms and their application in Python. It is a given that when it comes to science and computing algorithms have a key role to play. Apart from traditional computing, what’s required of a good programmer is the irreplaceable skill and capability to use algorithms to detect and solve real-world problems. This book covers basic topics such as fundamental algorithms which include sorting and searching to the latest algorithms which are used in machine learning and cryptography. 

PYTHON MACHINE LEARNING

By Sebastian Raschka is a fantastic introduction book for someone learning Python for the first time and doesn’t know where to start. This book dives deep into neural networks and is a highly recommended technical book. If someone who’s starting from scratch or if you’re someone who just wants to extend your data science knowledge, then this is an indispensable asset to have. 

THE ART OF DATA SCIENCE

By Elizabeth Matsui and Roger D. Peng is published with experienced data analysts who manage and conduct their own data analyses. This book simplifies the idea of data analyses in easy to understand terms and is very helpful to engineers as well as managers in the field of data science. 

BIG DATA: PRINCIPLES AND BEST PRACTICES OF SCALABLE REALTIME DATA SYSTEMS

By Nathan Marz and James Warren teaches you to construct big data systems using a design that takes advantage of hardware along with tools designed specifically to collect and analyze large volumes of data. It illustrated scalable, simple methods with which big data systems can be managed by a small team. Using practical methods, this book helps readers understand and implement big data while effectively managing them once constructed.

DATA MINING: CONCEPTS AND TECHNIQUES

(3rd edition) By Jiawei Han provides the concepts and techniques in processing gathered data or information, which has abundant applications. It touches various subjects from describing data mining, tools used in collecting data, usage and application of such data, preprocessing processing, data storage, online analytical processing (OLAP), and data cube technology It narrates the techniques of the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. The book is suitable for students, developers, professionals, as well as researchers who are interested in data mining.

Conclusion

It’s quite a long and tedious journey to becoming a data engineer – but it’s exciting and rewarding nonetheless. One needs a total understanding of tools, and techniques to bring out the best in any kind of data. The data engineering books can only help you learn while you apply your knowledge and skills. If you’re in it for the long haul then consider checking out these books, they might be the treasure you’ve been looking for. 

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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