15 Best Data Engineering Books (Updated for 2024)

Data has, unmistakably, sneaked into every industry’s day-to-day decision-making and product development processes, with over 87% of organizations considering analytics a top priority. The backbone of data operations—sourcing, processing, and warehousing—is the responsibility of data engineers, who build data pipelines, maintain data quality, and ensure proper ETL optimization.

While other compelling resources such as videos and Learning Paths exist, books are among the best alternatives for keeping up with rapidly developing tools and technologies as a data engineer. Books provide more in-depth coverage of advanced techniques, an end-to-end learning experience, and offline accessibility.

We reached out to our successful candidates to share the books they found most valuable on their journey to becoming data engineers. And, we’re excited to share 15 of the top recommendations with you:

Data Science on AWS by Chris Fregly and Antje Barth is a hands-on guide to implementing scalable, end-to-end machine learning (ML) and AI pipelines using Amazon Web Services (AWS). It covers critical tools like SageMaker, Lambda, and Kinesis, guiding users through real-world use cases such as fraud detection, NLP, and predictive maintenance. The book emphasizes cloud infrastructure’s flexibility and cost efficiency, automated ML workflows, and MLOps practices. It’s a comprehensive resource bridging cloud computing, data science, and AI deployment.

Key Concepts You’ll Learn

• Building ML pipelines on AWS
• Automating ML workflows with SageMaker
• Deploying scalable, cost-efficient ML solutions
• Best practices in MLOps and data security

Who Will Find It Most Useful

Data scientists, ML engineers, and AWS users seeking hands-on experience with cloud-based AI solutions.

What Users Didn’t Like

Some found the book overwhelming for beginners due to the dense material and prerequisite knowledge of AWS services.

Data Engineering with Python by Paul Crickard offers a practical and detailed guide on building data pipelines using Python. This book is ideal for readers aiming to gain a foundational understanding of data engineering concepts and best practices. Key topics covered include data preparation, data architecture, ETL (extract, transform, load) processes, and managing real-time data pipelines. Using Apache open-source tools and real-world examples, Crickard demonstrates how to deploy scalable data solutions for handling large datasets. Readers will learn critical skills such as data modeling, transforming data, staging and validation, and handling both structured and unstructured data.

Key Concepts You’ll Learn

• The basics of data engineering for supporting data science workflows
• Designing, scheduling, and monitoring data pipelines
• Data extraction, transformation, and enrichment
• Working with databases (both relational and NoSQL)
• Real-time data streaming with tools like Kafka and Spark
• Techniques for deploying and maintaining data pipelines in production

Who Will Find It Most Useful

This book is aimed at data analysts, ETL developers, and IT professionals transitioning into data engineering. Beginners without prior experience in data engineering will also find it accessible, as it covers foundational knowledge while advancing to complex topics.

What Users Didn’t Like

Some readers mentioned that while the book covers various topics, it occasionally lacks depth on complex subjects, making it less suitable for those seeking advanced, in-depth knowledge of specific data engineering tools or frameworks. Some also found the examples too simple or not sufficiently aligned with real-world challenges.

Spark: The Definitive Guide by Bill Chambers and Matei Zaharia is a comprehensive guide for anyone looking to learn or enhance their knowledge of Apache Spark. Written by the creators of Spark, this book focuses on big data processing and leveraging Spark’s capabilities to handle large datasets efficiently. The authors explore how to deploy and maintain Spark applications, delve into its core concepts like RDDs (Resilient Distributed Datasets) and DataFrames, and explain Spark’s powerful capabilities for both batch and real-time processing. The book also provides hands-on examples, making it an essential resource for learning Spark in the context of real-world applications.

Key Concepts You’ll Learn

• RDDs, DataFrames, and Spark’s execution engine
• How to handle large datasets with Spark’s distributed computing framework
• Using Spark Streaming for real-time data processing
• Leveraging Spark’s machine learning library for big data analytics
• How to deploy Spark in production environments and optimize its performance
• Integrating Spark with Hadoop, Hive, and other tools for big data processing

Who Will Find It Most Useful

This book is mainly written for data engineers and analysts looking to leverage Apache Spark for big data workflows. However, it adequately addresses the needs of developers who require an understanding of how to use Spark for large-scale data processing applications.

What Users Didn’t Like

Some readers felt that the book assumes familiarity with Spark, making it challenging for beginners without prior knowledge of distributed systems. A few users also mentioned that while the book is comprehensive, the examples might be too basic for those looking for more advanced use cases or deep-dive topics.

RESTful Web APIs: Services for a Changing World is a practical guide to designing flexible and scalable REST APIs. The book introduces the essential principles of REST and offers real-world examples of implementing them in API design. It emphasizes the importance of understanding REST’s architectural constraints, such as stateless communication and the use of hypermedia. The authors focus on evolving APIs over time and highlight strategies for ensuring they are usable and maintainable.

Key Concepts You’ll Learn

• The foundational rules of REST, including stateless communication and the client-server model
• The implementation of hypermedia to guide clients in interacting with your API efficiently
• Techniques to allow APIs to evolve over time without breaking existing clients
• Content negotiation and how different media types enhance API interactions
• Strategies for securing APIs and optimizing their performance in real-world applications

Database Internals by Alex Petrov provides an in-depth look at the inner workings of modern databases, with a focus on how distributed data systems operate. The book covers essential topics like storage engines, distributed systems, consistency models, and failure detection. It is an excellent resource for developers, database administrators, and engineers seeking a deeper understanding of how databases function. The author breaks down complex concepts using practical examples, discussions on database internals, and real-world case studies, enabling readers to build a solid foundation in both traditional and modern database architectures.

Key Concepts You’ll Learn

• The distinctions between B-trees and log-structured storage engines and their use cases
• The roles of Write-Ahead Logs (WAL), page cache, and buffer pools in efficient database storage
• How databases handle distributed systems, including communication patterns and consensus mechanisms
• Different consistency models used in distributed databases, such as eventual consistency, linearizability, and causal consistency
• How databases use leader election and failure detection algorithms to ensure reliability in distributed environments
• Log-structured merge trees (LSM trees) and their benefits in SSD-based systems

Who Will Find It Most Useful

This book is aimed primarily at database engineers, developers, and system architects who want to understand the complexities of database internals, particularly in distributed systems. It is also valuable for those working with both SQL and NoSQL databases who wish to grasp how different architectures and storage models affect performance, scalability, and consistency.

What Users Didn’t Like

Some data engineers mentioned that while the book is comprehensive, it may be overwhelming for those without a background in distributed systems or advanced databases. A few found the content a bit technical, and the dense explanations of algorithms and database architecture may be difficult for beginners to follow. Others felt that the examples, although thorough, could have been more practical for everyday applications.

Data Governance: The Definitive Guide by Evren Eryurek and Uri Gilad, is a comprehensive manual on establishing and maintaining a robust data governance framework. It provides a clear pathway for organizations to manage their data lifecycle, ensuring its quality, security, and accessibility while maintaining compliance with regulatory standards. The authors emphasize operationalizing data governance through people, processes, and technology, guiding readers in developing policies, assigning roles, and applying the necessary tools for effective governance.

The book covers data management, access, quality, and protection, helping organizations build trust in their data systems.

Key Concepts You’ll Learn

• How to create and implement a framework that ensures data quality and compliance across all stages of the data lifecycle
• Techniques for validating data quality, establishing data quality controls, and improving the reliability of data
• How to manage data effectively at every stage, from data creation and processing to archiving and destruction
• Critical strategies for securing sensitive data, including encryption, access control, and protecting data in transit
• Steps to implement data governance in your organization, focusing on people, processes, and technology for long-term success
• How to manage access rights and privacy in data systems, ensuring compliance with privacy laws such as GDPR

Who Will Find It Most Useful

This book is ideal for data engineers, data stewards, compliance officers, IT professionals, and anyone tasked with overseeing data governance in an organization. It’s especially beneficial for those looking to implement data governance frameworks, improve data quality, and ensure compliance with legal and regulatory requirements.

What Users Didn’t Like

Some readers felt that while the book is informative, it can be overly technical and assumes prior knowledge of data governance principles. Others noted that the book’s focus on tools and processes might be less helpful for those looking for a more strategic, high-level approach to data governance.

The Data Warehouse Toolkit by Ralph Kimball and Margy Ross is a definitive resource for designing and building data warehouses using dimensional modeling. As the cornerstone methodology for data warehousing, dimensional modeling helps transform raw business data into meaningful insights for decision-making. The book explains essential concepts such as fact tables, dimension tables, star schemas, and snowflake schemas. It offers detailed guidance on designing scalable and efficient data warehouses while addressing advanced topics like slowly changing dimensions (SCDs), ETL processes, and big data analytics. The third edition introduces enhanced modeling techniques and best practices, reflecting the latest industry trends.

Key Concepts You’ll Learn

• How to structure data warehouses using fact and dimension tables for efficient querying and analysis
• Techniques for managing and tracking changes in data over time to preserve historical accuracy
• How to design and optimize the extraction, transformation, and loading processes for data integration
• How to apply dimensional modeling in big data environments, including integration with tools like Hadoop and NoSQL systems
• Insights into how to involve business users in the modeling process to ensure the design aligns with their needs

Who Will Find It Most Useful

This book is invaluable for data architects, business intelligence professionals, data engineers, and anyone designing, building, or maintaining data warehouses. It is also helpful for business users who want to understand how data warehouses function to support decision-making and analytics.

What Users Didn’t Like

While the book is considered an essential guide, some readers noted that it can be dense and technical, making it difficult for beginners to follow. The examples might feel too abstract or theoretical for those looking for hands-on, practical applications. Additionally, some users felt the book’s coverage of cloud-based tools and newer technologies could be more comprehensive.

Designing Data-Intensive Applications is a highly regarded guide for understanding modern system architectures and database technologies. Author Martin Kleppmann takes readers through the fundamental principles of building reliable, scalable, and maintainable applications that handle large-scale data efficiently. The book balances theoretical concepts and real-world applications, exploring trade-offs in system design, data modeling, and distributed systems with clarity.

Key Concepts You’ll Learn

• Principles of relational, document, and graph data models
• Concepts behind B-trees, LSM-trees, and log-structured storage
• Handling replication, partitioning, and consistency across distributed systems
• From ACID and eventual consistency to isolation levels and serializability
• Mechanisms for achieving reliability in the face of system failures
• Techniques to partition and balance workloads efficiently

Who Will Find It Most Useful

This book is an essential resource for software engineers, system architects, and database professionals aiming to deepen their understanding of data systems. It is particularly beneficial for those working on distributed systems or tackling scalability challenges.

What Users Didn’t Like

Some users felt the content, while thorough, can be dense for beginners lacking a background in system design or databases. Others noted that the book is more theoretical and could benefit from additional practical, hands-on examples. Additionally, some concepts may require external research for deeper comprehension.

Snowflake Cookbook is a comprehensive guide for implementing and optimizing cloud-based data warehousing using Snowflake. Hamid Mahmood Qureshi and Hammad Warriach introduce Snowflake’s architecture, designed for scalability and performance in the cloud, and guide readers through best practices for configuring virtual warehouses, managing costs, and integrating Snowflake with other technologies. The content is highly practical, featuring hands-on exercises and examples tailored to modern cloud data management scenarios.

Key Concepts You’ll Learn

• Insight into Snowflake’s unique cloud-native structure and its scalability features
• How to transfer traditional data warehousing skills to the cloud using Snowflake
• Strategies for cost-effective and efficient data warehouse configurations
• Methods for staging data in object stores and loading it efficiently into Snowflake
• Integration with other tools and ecosystems for seamless workflows
• Practical examples of data processing and pipeline building in Snowflake

Who Will Find It Most Useful

This book is tailored for data warehouse developers, analysts, database administrators, and architects who want to implement or transition to a Snowflake-based environment. While some familiarity with database and cloud concepts is helpful, beginners with foundational knowledge can also benefit from the book’s structured explanations.

What Users Didn’t Like

Some readers noted that the book’s basic examples may not sufficiently address advanced use cases or deep-dive topics. A few reviews also mentioned a need for more in-depth discussions on troubleshooting and specific challenges in Snowflake implementations.

Data Pipelines Pocket Reference by James Densmore provides a practical and concise guide to building modern data pipelines tailored for analytics. It emphasizes ELT techniques, which have become standard in cloud-based environments, over the traditional ETL approach. The book walks through the end-to-end pipeline process, from data ingestion to orchestration, transformation, validation, and scaling, using real-world scenarios. It is written with both theory and practice in mind, offering insights into tools like Apache Airflow and strategies for maintaining efficient pipelines in diverse ecosystems.

Key Concepts You’ll Learn

• What data pipelines are and how they integrate into modern analytics workflows
• When to use each approach and the trade-offs involved
• Why ELT is more efficient for cloud-native infrastructures like Snowflake and Redshift
• Tools like Apache Airflow for building reliable and scalable pipelines
• Implementing Python frameworks for verifying data integrity and performance
• Insights into troubleshooting, monitoring, and scaling pipelines as data grows

Who Will Find It Useful

This book is particularly beneficial for data engineers transitioning to or working with modern cloud platforms, as it focuses on current industry-standard practices. Analytics engineers and data analysts seeking to broaden their knowledge of data pipeline mechanics will also find value, especially in sections covering orchestration and validation.

What Users Didn’t Like

Some readers noted that the book’s brevity meant it covered certain topics, like data orchestration and advanced transformations, at a surface level. This left experienced practitioners wanting deeper technical dives. Additionally, while beginner-friendly, professionals new to data engineering might find it challenging to fully grasp the intricacies of ELT and orchestration without prior context or hands-on experience.

Edited by Tobias Macey, 97 Things Every Data Engineer Should Know compiles insights from leading professionals in the field of data engineering. This book serves as a guide to the best practices, lessons learned, and philosophies of data engineering across diverse topics like data pipeline architecture, quality assurance, data integration, security, and cloud-based infrastructure. Each chapter is authored by a different expert, making it a multi-faceted resource for data engineers at all experience levels.

Key Concepts You’ll Learn

• Techniques for designing scalable, reusable, and efficient data pipelines
• Insights into ensuring high-quality data through robust validation processes
• Best practices for maintaining secure and compliant data workflows
• Strategies for managing modern data infrastructures in cloud environments
• Patterns that promote reusability and modularity in data workflows
• Perspectives on data lakes, silos, and the future of data architecture

Who Will Find It Most Useful

This book is a must-read for data engineers looking to expand their knowledge base or refine their approach to building and maintaining data systems. It’s also valuable for aspiring data engineers, data scientists, and software developers who want to gain insight into the challenges and solutions in modern data engineering. The diversity of contributors ensures it offers practical wisdom for professionals at any stage in their careers.

What Users Didn’t Like

Some readers found that the content varied significantly in depth and relevance due to the multi-author format. While this diversity is a strength, a few chapters were seen as overly introductory or less actionable. Additionally, readers seeking detailed technical tutorials or code-heavy examples may find the book’s conceptual focus less aligned with their needs.

Authored by Andriy Burkov, Machine Learning Engineering is a practical guide for applying machine learning in real-world engineering contexts. With insights drawn from Burkov’s extensive experience and industry leaders, the book delves into building reliable, scalable, and maintainable ML systems. It emphasizes best practices, system design patterns, and the nuances of production-level machine learning.

Key Concepts You’ll Learn

• Data acquisition to model deployment
• Strategies for managing large-scale operations
• Designing maintainable and fault-tolerant ML pipelines
• Techniques for efficient computation and resource use
• Practical examples of ML in production environments

Who Will Find It Most Useful

This book is ideal for professionals working in machine learning who aim to implement their solutions at scale, such as data scientists transitioning to ML engineering roles or seasoned ML practitioners refining their deployment skills. It provides actionable insights for solving industry-relevant challenges while maintaining technical relevancy.

What Users Didn’t Like

While widely praised for its clarity and depth, some readers felt the book assumes a level of prior knowledge, making it less accessible for beginners. Others noted that the focus on practical applications might leave out deeper theoretical discussions.

Michael Walker’s Python Data Cleaning Cookbook is a practical guide designed to help readers master modern data-cleaning techniques using Python. Through a recipe-based approach, the book covers essential tools and workflows for identifying, handling, and correcting messy or problematic data, providing actionable insights for various data analysis tasks. This resource emphasizes efficient data manipulation, visualization, and the creation of reusable functions for common cleaning tasks.

Key Concepts You’ll Learn

• Effectively assessing data structure, summarizing attributes, and filtering data
• Addressing common issues, like missing values, duplicates, and outliers, and handle inconsistent or invalid data
• How to use method chaining in pandas for productivity and develop user-defined functions and classes for automation
• Generating plots for exploratory data analysis to identify anomalies
• Techniques for detecting errors and ensuring data integrity
• Importing and cleaning data from various formats, including tabular, HTML, and JSON sources

Who Will Find It Most Useful

This book is ideal for data analysts, engineers, and anyone dealing with real-world datasets requiring extensive cleanup. While it is beginner-friendly for those with basic Python knowledge, the recipe-based approach also suits professionals looking for structured, efficient workflows to enhance their data-cleaning processes. Students and academics involved in data science projects will also benefit from its practical guidance and reproducible methods.

What Users Didn’t Like

Some readers mentioned that the book assumes familiarity with Python and pandas, which could challenge complete beginners. Others noted that while the content is thorough, it may feel repetitive or overly basic for advanced users seeking innovative techniques or domain-specific solutions. Additionally, a few reviews pointed out a lack of emphasis on real-world case studies to contextualize the methods.

Fundamentals of Data Engineering provides a thorough introduction to building modern, scalable data systems. Authors Joe Reis and Matt Housley detail the data engineering lifecycle, covering data generation, ingestion, orchestration, transformation, storage, and governance. The book bridges theoretical principles with practical guidance, including frameworks for selecting the right technologies. Through comprehensive coverage of both traditional and modern practices, it equips readers to create robust and efficient data pipelines.

Key Concepts You’ll Learn

• The end-to-end data engineering process
• Evaluating and integrating the best tools for your data systems
• Designing data pipelines for efficiency and reliability
• Implementing strategies for secure and compliant data use
• Cloud-native tools, event-driven systems, and real-time processing architectures

Who Will Find It Most Useful

This book is ideal for data engineers, architects, and software developers seeking a comprehensive foundation in modern data engineering. It caters to professionals transitioning into data engineering roles or expanding their expertise in scalable and distributed systems. With its practical examples, it is also useful for organizations building cloud-first data architectures.

What Users Didn’t Like

Some readers felt the book’s focus on conceptual overviews left them wanting deeper technical details or more concrete examples. Others noted that while the book emphasizes modern practices, it may not be as useful for professionals working in more traditional, on-premise environments. A few found the pace too fast for beginners unfamiliar with data systems.

Streaming Systems by Tyler Akidau and Slava Chernyak explores the intricacies of streaming data processing, offering a deep dive into managing real-time, unbounded data at scale. Authored by industry experts from Google, it provides a robust framework for understanding streaming systems conceptually and practically, focusing on principles like watermarks, exactly-once processing, and integrating streams and tables. Expanded from Tyler Akidau’s popular blog series “Streaming 101” and “Streaming 102,” it serves as both a foundational text and an advanced resource on data streaming.

Key Concepts You’ll Learn

• Contrasts and use cases for both paradigms
• Including out-of-order data handling, watermarks, and exactly-once processing
• Foundations for combining batch and streaming processes
• Real-world applications of stream processing for dynamic datasets
• Links between SQL, relational algebra, and stream processing
• Practical applications in processing continuous data streams

Who Will Find It Useful

This book is ideal for data engineers, data scientists, and software developers involved in real-time data processing. It’s particularly valuable for professionals transitioning to streaming systems from traditional batch processing or those building scalable, fault-tolerant data pipelines.

What Users Didn’t Like

Some readers noted that the content can be challenging for those new to distributed data systems, as it assumes prior experience with the fundamentals. Others mentioned that the examples, while illustrative, might not delve deeply enough into advanced production-level scenarios.

Explore more helpful data engineer learning resources from Interview Query: Data Science Course, Top Data Engineer Questions for 2024, Python Questions for Data Engineers, Data Engineer Case Study Interview Questions and Guide, and Data Engineering Projects.