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Top 8 Data Engineering Books [Beginners to Advanced]
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27th Sep, 2023
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The demand for experienced data engineers continuously expands in today's data-driven environment. Whether you're a beginner looking to dive into the foundations or an experienced practitioner seeking advanced techniques, the right books can be your guiding light. Books on data engineering serve as essential resources to guide you through the vast terrain of data engineering.
Whether you're an aspiring data engineer trying to start a lucrative career or a seasoned expert looking to improve your skills, Azure data engineer books can be beneficial. The best books on data engineering will provide you with a solid foundation and help you acquire the skills you need to flourish in data engineering.
This article suggests the top eight data engineer books ranging from beginner-friendly manuals to in-depth technical references.
What is Data Engineering?
The practice of designing, building, and maintaining the infrastructure and systems required to collect, process, store, and deliver data to various organizational stakeholders is known as data engineering. It refers to a series of operations to convert raw data into a format suitable for analysis, reporting, and machine learning which you can learn from data engineer books.
You can pace your learning by joining data engineering courses such as the Bootcamp Data Engineer. This comprehensive program provides thorough instruction in data engineering ideas, tools, and best practices.
Who are Data Engineers?
Data engineers are experts who specialize in the design and execution of data systems and infrastructure. They have unique skills, including competence in software engineering, data management, and analytics.
Top 4 Data Engineer Books for Beginners
The data engineering books for beginners provide comprehensive guidance and practical insights into the essential concepts and techniques of data engineering. Here we have mentioned some of the best data engineering books for beginners you can find:
Fundamentals of Data Engineering: Plan and Build Robust Data Systems
This book provides aspiring data engineers with the fundamental knowledge required to launch a successful career in the industry.
Author Name: Joe Reis and Matt Housley
Year of Release: 2022
Goodreads Rating: 4.32 /5 (based on 180 ratings)
Publisher: O'Reilly
Book Info:
Paperback: 426 pages
Language: English
Overview:
Joe Reis and Matt Housley's Fundamentals of Data Engineering: Plan and Build Robust Data Systems is a crucial tool for beginners who want to comprehend the basics of data engineering. The essential theories, procedures, and equipment for creating trustworthy and effective data systems are covered in this book. It explores subjects including data modeling, data pipelines, data integration, and data quality, offering helpful advice on organizing and implementing reliable data solutions.
Key Benefits and Takeaways:
Learn the fundamental principles of data engineering.
Gain knowledge on designing and constructing scalable data systems capable of handling massive data.
Understand the significance of data engineering in facilitating data-driven decision-making processes.
Data Engineering with Python
Data Engineering with Python" equips learners with the skills they need to get started with data engineering using the powerful Python programming language.
Author Name: Paul Crickard
Year of Release: 2020
Goodreads Rating: 3.44 /5 (based on 16 ratings)
Publisher: Packt Publishing
Book Info:
Kindle Edition: 464 pages
Language: English
Overview:
Paul Crickard's Data Engineering with Python is a comprehensive guide that introduces beginners to the field of data engineering using Python. This book provides a hands-on approach to data engineering ideas, strategies, and tools. Readers will learn to design fast data pipelines and conduct analytical tasks using Python for data engineering.
Key Benefits and Takeaways:
Understand data intake strategies and data transformation procedures by learning data engineering principles with Python.
Investigate alternative data storage solutions, such as databases and data lakes.
Acquire first-hand experience in learning Python packages for data processing and analysis.
Big Data: Principles and best practices of scalable real-time data systems
Big Data: Principles and Best Practices of Scalable Realtime Data Systems is an excellent resource for anyone who wants to learn the fundamentals of working with big data.
Author Name: Nathan Marz, James Warren
Year of Release: 2012
Goodreads Rating: 3.84/5 (based on 476 ratings)
Publisher: Manning Publications
Book Info:
Paperback: 328 pages
Language: English
Overview:
Nathan Marz and James Warren's "Big Data: Principles and Best Practices of Scalable Realtime Data Systems" thoroughly discusses the principles and best practices of working with big data. The book focuses on developing scalable and real-time data systems, covering data modeling, processing, and distributed systems. It covers popular technologies such as Apache Kafka, Apache Storm, and Apache Hadoop, giving users practical advice on developing and executing effective data pipelines.
Key Benefits and Takeaways:
Learn the core concepts of big data systems.
Investigate real-time data processing methods by employing distributed systems.
Master the art of data modeling and developing scalable data architectures.
Spark: The Definitive Guide: Big Data Processing Made Simple
Spark: The Definitive Guide: Big Data Processing Made Simple is a must-have reference for individuals wishing to get started with Apache Spark.
Author Name: Bill Chambers, Matei Zaharia
Year of Release: 2018
Goodreads Rating: 4.17 /5 (based on 229 ratings)
Publisher: O'Reilly Media
Book Info:
Kindle Edition: 606 pages
Language: English
Overview:
Bill Chambers and Matei Zaharia's book Spark: The Definitive Guide: Big Data Processing Made Simple thoroughly introduce Apache Spark, a powerful large data processing platform. Ingestion of data, processing of data, machine learning, and graph processing are a few topics covered in the book. With helpful illustrations and thorough explanations, it assists readers in comprehending how to use Spark for big data processing and analytics applications.
Key Benefits and Takeaways:
Learn everything there is to know about Apache Spark and its ecosystem.
Investigate Spark's batch processing, streaming, and machine learning capabilities.
Deepen your understanding of Spark SQL, DataFrame API, and Spark Streaming.
Top 4 Advanced Data Engineering Books
Here, we have mentioned some of the top 4 advanced data engineer books:
Designing Data-Intensive Applications
Designing Data-Intensive Applications is an excellent reference for creating robust and scalable data systems.
Author Name: Martin Kleppmann
Year of Release: 2015
Goodreads Rating: 4.71/5 (based on 7160 ratings)
Publisher: O'Reilly Media
Book Info:
eBook: 526 pages
Language: English
Overview:
The comprehensive manual Designing Data-Intensive Applications by Martin Kleppmann, discusses the design principles and methods used to create reliable and scalable data-intensive applications. Kleppmann explores the complexities of creating data systems that are dependable, effective, and maintainable with a focus on real-world examples and scenarios.
Key Benefits and Takeaways:
Discover the many data storage and processing methods, including databases, caches, and messaging systems.
Investigate the difficulties and solutions in developing distributed systems and ensuring data consistency.
Learn about data analysis techniques, data integration, serialization, and data pipelines.
The Data Warehouse Toolkit
It provides a comprehensive overview of the key concepts and techniques to design and construct data warehouses.
Author Name: Ralph Kimball, Margy Ross
Year of Release: 1996
Goodreads Rating: 4.16 /5 (based on 864 ratings)
Publisher: Wiley
Book Info:
Paperback: 600 pages
Language: English
Overview:
Ralph Kimball and Margy Ross's The Data Warehouse Toolkit guide provides practical insights into developing and building data warehouses. The book covers core data warehousing principles, techniques, and best practices such as dimensional modeling, ETL (Extract, Transform, Load) procedures, and data quality. With a step-by-step approach, it provides readers with an understanding of creating effective data warehouse solutions that support business intelligence and analytics.
Key Benefits and Takeaways:
Master the fundamentals and techniques of dimensional modeling for data warehouses.
Acquire best practices for building and deploying ETL processes for data warehouse populations.
Explore strategies for maintaining data quality and controlling data lineage in a data warehouse.
Building a Data Warehouse: With Examples in SQL Server
This book provides readers with the information and abilities to build and implement a reliable data warehouse solution with its realistic examples and thorough coverage of data warehousing topics.
Author Name: Vincent Rainardi
Year of Release: 2007
Goodreads Rating: 3.89/5 (based on 19 ratings)
Publisher: Wiley
Book Info:
Paperback: 540 pages
Language: English
Overview:
Building a Data Warehouse: With Examples in SQL Server by Vincent Rainardi is a detailed reference on designing and implementing a data warehouse with SQL Server. Data modeling, ETL (Extract, Transform, Load) processes, dimensional modeling, and data visualization are among the topics covered in the book. Rainardi offers readers a thorough knowledge of developing, creating, and maintaining a data warehouse through practical examples and step-by-step guidance.
Key Benefits and Takeaways:
Discover the principles of data warehousing and its significance in today's corporate situations.
Understand the design and implementation of a data warehouse using SQL Server.
Investigate methods for extracting, processing, and loading data into a data warehouse.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
This book equips readers with the skills to properly analyze and visualize data using R through practical examples and clear explanations.
Author Name: Garrett Grolemund, Hadley Wickham
Year of Release: 2016
Goodreads Rating: 4.56/5 (based on 1,036 ratings)
Publisher: O'Reilly Media
Book Info:
Paperback: 522 pages
Language: English
Overview:
Garrett Grolemund and Hadley Wickham's R for Data Science: Import, Tidy, Transform, Visualize, and Model Data is an in-depth guide on utilizing the R programming language for data science tasks. Data importing, data wrangling, data visualization, and statistical modeling are among the techniques and concepts covered in the book. The authors effectively teach how to use R's capabilities for successful data analysis and visualization through practical examples and hands-on exercises.
Key Benefits and Takeaways:
Learn how to import, clean, and transform data using R.
Investigate data visualization strategies to obtain insights and share results.
Discover statistical modeling and machine learning in R.
Preparation Tips for Data Engineering
Here are some important preparation recommendations for succeeding in data engineering:
Master Programming Languages
Consider enrolling in a boot camp for data engineering. These rigorous and immersive programs offer hands-on training and coaching from industry experts.
Deep Dive into SQL
Discover SQL syntax, query optimization techniques, and database design fundamentals. Write complex SQL queries and optimize them for improved performance.
Familiarize Yourself with Big Data Technologies
Be familiar with their architectures, components, and use cases. Get hands-on experience by working on projects or following online tutorials.
Continuously Learn and Stay Curious
To broaden your knowledge and skills, read books, follow blogs, join online communities, and engage in data engineering initiatives. You can learn about cloud technologies from AWS data engineer books.
Sharpen Problem-Solving Abilities
Improve your problem-solving abilities by practicing algorithmic thinking, overcoming data challenges, and looking for inventive solutions.
More Ways to Learn About Data Engineering
Various excellent strategies for improving your abilities and understanding of data engineering exist. Here are some other options for learning about data engineering, like boot camps, online courses, and specialist programs. Data Course online programs provide data engineering training. One can examine courses on big data technologies, ETL methods, data warehousing, and cloud platforms by referring to the GCP data engineering book.
Wrapping Up
Data engineering is critical in organizing and translating data into valuable insights. It is a fast-growing profession that necessitates a blend of technical knowledge, practical abilities, and a thorough understanding of data management principles which you can get through data engineering best books.
Also, you can find several Google data engineer books for beginners. Individuals can establish a strong foundation in data engineering by reading books for data engineering and exploring numerous learning channels such as KnowledgeHut‘s bootcamp data engineer, online courses, and collaborative learning platforms.
Frequently Asked Questions (FAQs)
1. What should I study for data engineering?
Python language, databases, big data, ETL processes, data warehousing, cloud, and data security are needed to excel in data engineering.
2. Is Python enough for data engineers?
Python is widely used in data engineering due to its versatility and extensive libraries. It is a powerful language for data manipulation, ETL processes, and scripting. However, while Python is valuable, it is also recommended to know other programming languages.
3. Should I learn Python or SQL first?
The decision between learning Python or SQL depends on your goals and the career you wish to pursue. However, considering the significance of SQL, it is often recommended to begin with SQL.
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