Hey? are you tired of searching books for Data Engineering ?? If yes, then you have landed in the right place. We will talk about the Best 10 books for Data Engineering.

Books are considered more accurate, more careful, and objective as compared to videos. On the other hand, videos, blogs are time efficient and more convenient options.

“The goal is to turn data into information, and information into insight.”

Carly Fiorina

So, why wait? Let’s Loop onto the 10 Best Books for Data Science Engineering

1) DATA ENGINEERING WITH PYTHONPaul Crickard

Source: Google

Why read this book?

Data engineering provides the foundation for data and mathematical science and forms an integral part of every business. This manual will help you to explore the various tools and methods used to understand the data engineering process using Python.

This book will show you how to deal with the challenges you often face in various aspects of data engineering. It will begin with an introduction to the basics of data engineering, as well as the technologies and frameworks needed to build data pipelines to work with large databases. You will learn to convert and clean data and do the math to get the most out of your data. As you progress, you will discover how to work with a wide variety of complex data and production websites, and build data pipelines. Using real-world examples, you will build structures where you will learn how to use data pipelines.

By the end of this Python book, you will have gained a clearer understanding of data modeling techniques, and you will be able to confidently build data engineering pipelines to track data, use quality testing, and make the necessary changes in production.

Advantages of this book

  • Firstly it is very well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples.
  • Design data models and learn how to extract, transform, and load (ETL) data using Python.
  • Schedule, automate, and monitor complex data pipelines in production.

2) Designing Data – Intensive Applications – Martin Kleppmann

Source: Google

Why read this book?

Data is among the many challenges in system design today. Serious problems need to be considered, such as balance, consistency, reliability, efficiency, and maintenance. Additionally, we have a variety of amazing tools, including affiliate websites, NoSQL data stores, streams or batch processors, and message clients. What are the appropriate decisions for your application? How do you make sense of all these words?

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse world by exploring the pros and cons of various data processing and storage technologies. The software is constantly changing, but the basic principles remain the same. Through this book, software engineers and developers will learn how to apply those ideas to practice, as well as how to make the most of data in modern applications.

Advantages of this book

  • Peer under the hood of the systems you already use, and learn how to use and operate them more effectively.
  • Make informed decisions by identifying the strengths and weaknesses of different tools.
  • Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity.
  • Understand the distributed systems research upon which modern databases are built.
  • Peek behind the scenes of major online services, and learn from their architectures.

3) Spark: The Definitive Guide: Big Data Processing Made Simple – Bill Chambers, Matei Zaharia

Source: Google

Why read this book?

Learn how to use, and store Apache Spark with this comprehensive guide, written by creators of open-source cluster computing. With an emphasis on development and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia divide Spark titles into separate categories, each with different goals.

You will explore the basic functions and common functions of Spark Organized APIs, as well as Scheduled Broadcast, a new high-level API for building live streaming applications. Engineers and system administrators will learn the basics of monitoring, tuning, and correcting Spark errors, and explore machine learning strategies and conditions for hiring MLlib, a fast-paced Spark machine learning library.

Advantages of this book

  • Firstly ,it helps you to learn about DataFrames, SQL, and Datasets-Spark’s core APIs – with examples used.
  • Access low-level Spark APIs, RDDs, and use SQL and DataFrames.
  • Understand how Spark runs on a cluster.
  • Debug, monitor, and tune Spark clusters and applications.
  • Learn the power of Organized Streaming, Spark’s-stream-processing engine.
  • Learn how you can apply MLlib to a variety of problems, including classification or recommendation.

4) Data Science For Dummies – Lillian Pierson, Jake Porway

Source: Google

Why read this book?

Data science jobs are expected to surpass the number of people with data science skills — enabling those with the knowledge to fill the data science field into a hot commodity in the coming years. Data Science For Dummies is an excellent start for IT professionals and students who are interested in making sense of the organization’s large data sets and applying their experience in real-world business situations.

From disclosing rich data sources to managing large amounts of data within computer hardware and software limits, ensuring compliance in reporting, integrating various data sources, and more, you will develop the information you need to successfully translate data and tell a story. understood by anyone in your organization.

Advantages of this book

  • Firstly it has background in fundamentals of data science and preparing your data for analysis.
  • Provides different data visualization techniques .
  • It explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques.

5) The Data Warehouse Toolkit – Ralph Kimball, Margy Ross

Source: Google

Why read this book?

This book is for Data Engineering which offers an overview of all the good and the modern and current trends and includes a clear discussion of new topics such as big data. This book also incorporates new and improved star schema model patterns.

There are two new chapters in this book on ETL strategies. All in all, this is a good book to understand how data repositories work.

Advantages of this book

  • Firstly, the Authors are known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence.
  • Secondly ,this book starts-off with fundamental design recommendations and then through increasingly complex scenarios.
  • Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more.
  • Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more.

6) Building a Data Warehouse: SQL Servers – Vincent Rainardi

Source: Google

Why read this book?

In this booklet, you will learn how to build a database, which includes defining structures, understanding how to do it, collecting needs, designing data models, and creating information.

This book focuses on STL Server-based ETL processes and contains hundreds of virtual, virtual reality scenarios. You will also learn how to present data to users using reports and websites of various sizes.

Advantages of this book

  • The only book that shows how to implement a data warehouse using SQL Server.
  • Interest in this topic for SQL Server is high and poorly understood.
  • The code in the book will save companies hundreds of hours of development time and many wrong turns.
  • Despite the intensity of the code, managers to programmers will find the book extremely useful.
  • This book will be good for SQL Server 2005 and SQL Server 2008.

7)Big Data: Principles and best practices of scalable real-time data systems – Nathan Marz, James Warren

Source: Google

Why read this book?

Web-based applications such as social networks, real-time statistics, or e-commerce sites that deal with large amounts of data, their capacity and speed exceed the limits of standard web applications. These applications require structures built near mechanical clusters to store and process data of any size, or speed. Fortunately, scale and simplicity are inseparable.

Big Data teaches you to build large data systems using an architecture designed specifically for capturing and analyzing web-scale data. This book introduces Lambda Architecture, a fast, easy-to-understand method that can be developed and operated by a small team. You will explore the theory of big data systems and how you can apply it effectively. In addition to getting a standard framework for processing big data, you will learn some technologies like Hadoop, Storm, and NoSQL websites.

Advantages of this book

  • Introduction to big data systems.
  • Real-time processing of web-scale data.
  • Tools like Hadoop, Cassandra, and Storm.
  • Extensions to traditional database skills.

8)HADOOP: THE DEFINITIVE GUIDE – Tom White

Source: Google

Why read this book?

Get ready to unlock your data. With the fourth edition of this complete guide, you will learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for editors who want to analyze data sets of any size and for managers who want to set up and use Hadoop collections.

Using Hadoop 2 exclusively, author Tom White introduced new YARN chapters and several Hadoop-related projects such as Parquet, Flume, Crunchand Spark. You will learn about the latest changes in Hadoop and explore new case studies about Hadoop’s role in health care systems and genomics data analysis.

Advantages of this book

  • Learn fundamental components such as MapReduce, HDFSand YARN
  • Explore MapReduce in depth, including steps for developing applications with it
  • Set up and maintain a Hadoop cluster running HDFS and MapReduce on YARN
  • Learn two data formats: Avro for data serialization and Parquet for nested data
  • Use data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer)
  • Understand how high-level data processing tools like Pig, Hive, Crunchand Spark work with Hadoop
  • Learn the HBase distributed database and the ZooKeeper distributed configuration service

9)Rebuilding Reliable Data Pipelines Through Modern Tools – Ted Malaska

Source: Towards Data Science

Why read this book?

This book teaches participants in the data space and what the ETL (Extract, Transform, Load) data landscape looks like.

It uses a lot of simple but effective metaphors to ‘feel’ what it would be like to work as a data engineer in the area described in the book.

There is a detailed book written by the same author, Ted Malaska, but I think this short book will suffice as a basis for your knowledge and you can find your way by exploring.

Advantages of this book

  • How performance management software can reduce the risk of running modern data applications
  • Methods for applying AI to provide insights, recommendations, and automation to operationalize big data systems and data applications
  • How to plan, migrate, and operate big data workloads and data pipelines in the cloud and in hybrid deployment models

10)Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines – Chris Fregly and Antje Barth

Source: Google

Why read this book?

This is an end-to-end book, but data engineers will find here a solid introduction to building cloud pipelines in AWS. In particular, the focus is on AI pipelines and machine learning programs, which include natural language processing, fraud detection, and computer-aided visualization.

All the authors spray with details to help reduce costs and improve plumbing performance. Finally, the guide integrates all the concepts together, provides a duplicate machine-readable pipeline blueprint, and creates an important guide for everyone measuring AWS AI pipelines.

Advantages of this book

  • How the Amazon AI and ML stacks apply to real-world cases like fraud detection.
  • Practical step-by-step use cases.
  • Amazon AWS pipelines.
  • Scaling operations pipelines in AWS.
  • Data ingestion techniques.

Conclusion:

So, in this blog, we discussed the 10 Best Books for Data Engineering. All of these are a masterpiece and you can learn a lot from them.

Hope you found the blog helpful!