9 Data Engineering Books Every Aspirant Must Read in 2025

Data engineering is one of the fastest-growing and most in-demand jobs in the tech industry today. Companies are collecting massive amounts of data, and they need skilled data engineers to build the infrastructure and pipelines required to process, store, and analyze it all.

If you‘re looking to break into this lucrative field, one of the best ways to build your skills is by reading books written by experienced data engineering practitioners and experts. But with so many data engineering books out there, which should you prioritize?

To help you zero in on the most essential and valuable reads, I‘ve compiled this list of the 9 data engineering books every aspirant must read in 2024. Whether you‘re a complete beginner or already have some experience, these books cover critical data engineering concepts and skills with depth, insight, and engaging real-world examples.

1. Fundamentals of Data Engineering by Joe Reis and Matt Housley

Kicking off our list is this practical and comprehensive guide to the core principles and technical practices of data engineering. The authors, Joe Reis and Matt Housley, are experienced data engineers who have worked at top companies like Apple, Netflix, Google, and Microsoft.

In the book, they share their hard-earned wisdom and walk you through the essential concepts and skills required to succeed as a data engineer. You‘ll learn how to:

• Collect, transform, and serve data
• Design data pipelines and architectures
• Work with massive datasets
• Automate data workflows
• Handle real-time streaming data
• And much more

One of the book‘s key strengths is how it ties data engineering concepts to actual business needs. You‘ll see how the data infrastructures you build can provide real value to companies and end users.

Filled with practical advice, helpful diagrams, and interesting case studies from the authors‘ professional experience, Fundamentals of Data Engineering is a must-read for anyone beginning their data engineering journey. It provides a rock-solid foundation to build upon.

2. Designing Data-Intensive Applications by Martin Kleppmann

Next up is a true classic of the data engineering world. Widely regarded as the "data engineer‘s bible", Martin Kleppmann‘s Designing Data-Intensive Applications is an essential read for both beginners and experienced practitioners alike.

Kleppmann, a researcher at the University of Cambridge and former engineer at LinkedIn, dives deep into the tricky problems you encounter when building data-intensive distributed systems. The book covers essential topics like:

• Data models and query languages
• Storage engines and indexing
• Replication, partitioning, and consensus
• Batch and stream processing
• Distributed system glitches and failures

Rather than just teaching you specific tools, the book focuses on timeless, high-level principles that can guide your decision making for any data-intensive application. Kleppman excels at clearly explaining complex technical concepts and revealing the subtle trade-offs between different approaches.

While the book is not a light read, it rewards careful study with a masterful overview of the key concepts that underpin modern data engineering. Every data engineer should read and regularly revisit this book.

3. Data Pipelines Pocket Reference by James Densmore

Data pipelines are one of the core components of any data architecture. These are the systems that extract data from source systems, transform it into usable formats, and load it into target storage systems for analysis or serving.

In the Data Pipelines Pocket Reference, data engineer James Densmore provides a concise and practical guide to building pipelines with the latest open-source frameworks and tools. You‘ll learn how to:

• Extract data from a variety of sources like databases, APIs, and streams
• Perform transformations using SQL, Spark, and other data processing tools
• Handle incremental updates and reprocessing
• Test, monitor, and troubleshoot your pipelines
• Orchestrate end-to-end workflows with tools like Airflow and Prefect

While the book is a quick read, it covers a lot of essential ground and helpful tips for working with modern data pipeline tools. The handy diagrams, code snippets, and cheat sheets make it a valuable quick reference to keep by your side while building pipelines on the job.

4. Streaming Systems by Tyler Akidau, Slava Chernyak & Reuven Lax

Real-time data streams are becoming increasingly critical for modern data-driven applications. Streaming data allows companies to gain live insights and automate instant responses to events as they happen. But working with unbounded, always-updating data streams is very different from batch-oriented data workflows.

In Streaming Systems, three experienced data engineers from Google provide a deep dive into the tools and techniques required to work with real-time streaming data. You‘ll learn:

• The core principles and concepts of streaming systems
• How to design streaming pipelines for collection, storage, and processing
• Strategies for dealing with time, state, and out-of-order data
• How to provide end-to-end reliability and consistency
• Advanced techniques like sessionization, windowing, and watermarking

The book strikes a nice balance between high-level concepts and low-level details. It builds up your streaming intuition while also diving into specifics of how to implement production-grade pipelines with tools like Apache Beam, Flink, and Kafka.

5. Data Mesh by Zhamak Dehghani

Data mesh is one of the hottest new architectural paradigms that‘s been gaining traction since it was proposed by ThoughtWorks consultant Zhamak Dehghani in 2019.

The core idea of data mesh is to move away from centralized, monolithic data platforms and pipelines. Instead, it advocates for distributing data ownership to cross-functional teams throughout an organization, with each team responsible for providing their data as a product, including quality, documentation, and SLAs.

Dehghani‘s book provides a thorough overview of the principles and motivations behind the data mesh, contrasting it with traditional centralized data architectures. It walks through how to design a data mesh architecture and incrementally adopt a mesh within your organization.

Data mesh is still a cutting-edge approach and won‘t be right for every company. But its focus on decentralization and data as a product is influencing data architectures everywhere. This book is essential reading for senior data engineers looking to understand the future of the field.

6. Cloud Data Engineering by Saurabh Gupta & Aditya Toshniwal

Today, nearly every company is moving its data infrastructure to the cloud to reduce costs, improve availability, and scale effortlessly. But data engineering in the cloud comes with its own unique set of challenges and best practices.

Cloud Data Engineering is a practical and code-focused guide to building data infrastructure on AWS, Azure, and GCP. In it, experienced cloud data engineers Saurabh Gupta and Aditya Toshniwal walk you through:

• Designing data lakes, warehouses, and pipelines in the cloud
• Working with cloud databases and autoscaling clusters
• Leveraging serverless technologies for ETL and compute
• Securing, monitoring, and deploying your cloud data infrastructure
• Cost and performance optimization techniques

One of the book‘s top selling points is the detailed code examples, which show you exactly how to implement each component of a cloud data platform. Whether you‘re just getting started with the cloud or looking to optimize your company‘s existing deployments, this book is filled with useful patterns and practices.

7. Data Engineering with Python by Paul Crickard

Python has become the lingua franca of data science and analytics. It‘s also increasingly used to build scalable data pipelines and architectures. For data engineers looking to master Python, Data Engineering with Python by Paul Crickard is the perfect resource.

The book dives into key Python skills and libraries for data engineering, including:

• Building data ingestion and processing pipelines with Pandas
• Working with databases and SQL using Python
• Scaling workloads with parallel and distributed computing in Python
• Workflow orchestration with Luigi, Airflow, and Prefect
• Leveraging Apache Spark with PySpark for big data processing
• Productionizing machine learning models

Throughout the book, Crickard demonstrates concepts using hands-on code examples and real-world datasets. Exercises at the end of each chapter reinforce your learning. By the end, you‘ll be ready to tackle most data engineering tasks using Python.

8. Terraform: Up and Running by Yevgeniy Brikman

Infrastructure-as-code, or provisioning cloud resources via declarative configuration files, has become a key practice for deploying and managing data platforms. And Terraform is one of the most popular and flexible IAC tools available today.

In Terraform: Up and Running, Gruntwork co-founder Yevgeniy Brikman shows you how to define, provision, and manage production-grade cloud infrastructure using Terraform. You‘ll learn how to:

• Write Terraform configurations for AWS, Azure, and GCP
• Automate Terraform workflows and CI/CD pipelines
• Manage Terraform state across teams
• Create reusable infrastructure modules
• Test and security infrastructure code
• Scale up to managing complex cloud architectures

The book is filled with practical tips and best practices forged from the author‘s extensive experience using Terraform to manage infrastructure at scale. For data engineers involved in deploying and managing data platforms, these infrastructure-as-code skills are becoming increasingly essential.

9. Building Data Science Applications with FastAPI by François Voron

Our final pick focuses on the crucial "last mile" of data engineering: building applications and APIs to serve up data to end users. Increasingly, data engineers are responsible for this step, working closely with data scientists to productionize machine learning models.

In the book, François Voron shows you how to use the FastAPI web framework to quickly build and deploy production-ready data science applications. You‘ll learn how to:

• Define REST APIs using Python type hints
• Ingest and process data asynchronously
• Integrate and serve machine learning models
• Secure and monitor your APIs
• Test and deploy data applications

Although a relatively thin book, it covers everything you need to know to put your data and models to work in user-facing applications. In our API-driven world, these are critical skills for data engineers to pick up.

Conclusion

So there you have it—9 data engineering books that every aspirant should read in 2024. Collectively, they cover the key skills and practices that data engineers need to be successful, including:

• Data modeling, storage, and architecture
• Data pipelines and ETL processes
• Real-time stream processing
• Big data tools and platforms
• Cloud infrastructure and automation
• Workflow orchestration
• Data applications and APIs

While you certainly don‘t need to read every book cover-to-cover, each provides a deep dive into a critical area of the field. I recommend using this list to fill in gaps in your knowledge and level up your skills in areas important to your work.

Remember, data engineering is a vast and fast-moving field. New tools and approaches are always emerging. The best data engineers adopt a mindset of continuous learning, regularly reading books and articles to stay on top of the latest trends and hone their craft.

What are your favorite data engineering reads? Feel free to share them in the comments!