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Authors: Joe Reis & Matt Housley
Publisher: O’Reilly Media (2022) · Language: English · ISBN-10: 1098108302 · ISBN-13: 978-1098108304
Fundamentals of Data Engineering: Plan and Build Robust Data Systems (1st Edition) offers a comprehensive, modern foundation for understanding how to design, build, and maintain scalable and reliable data systems.
Written by industry experts Joe Reis and Matt Housley, this book goes beyond buzzwords to deliver a practical, real-world framework for data engineers, data architects, analysts, and developers who want to master modern data infrastructure.
Covering every phase of the data engineering lifecycle—from data ingestion and storage to processing, transformation, and delivery—it provides actionable insights for professionals working with cloud platforms, analytics, and big data technologies.
Understand the end-to-end data lifecycle, learning how data is collected, structured, processed, and consumed. The authors provide a clear, systems-level understanding of how to build efficient, maintainable, and future-proof architectures.
Master the design and management of batch and streaming pipelines using modern data tools such as Apache Airflow, Kafka, dbt, and Spark. Learn best practices for automation, scaling, and workflow orchestration.
Develop solid skills in data modeling for analytical, operational, and machine learning systems. Explore data lakes, warehouses, and lakehouse designs optimized for performance and scalability.
Explore the rapidly evolving world of cloud-native data architectures. The book covers best practices for deploying and managing systems on AWS, Azure, and Google Cloud, including hybrid and multi-cloud strategies.
Learn how to implement data governance, observability, and monitoring frameworks to maintain trust in your data. Practical techniques help ensure accuracy, lineage, and compliance across your data ecosystem.
Through case studies and applied examples, see how top organizations use robust data engineering principles to support analytics, AI, and business intelligence at scale.
In today’s data-driven world, data engineers are at the core of every technology-driven business. Fundamentals of Data Engineering fills a critical gap between software engineering and data science, helping readers build the technical and strategic mindset needed to handle data at scale.
Unlike surface-level tutorials, this book focuses on core principles, architectural patterns, and practical implementation, ensuring readers can adapt to new technologies while maintaining solid engineering fundamentals.
Data Engineers building and optimizing data pipelines.
Data Architects designing modern data platforms.
Software Developers moving into data-focused roles.
Data Scientists and Analysts seeking to understand underlying data systems.
Students and Educators exploring the structure of modern data ecosystems.
The data engineering lifecycle
ETL and ELT system design
Batch vs. streaming processing
Data modeling and architecture
Cloud data platforms (AWS, Azure, GCP)
Data lakes, warehouses, and lakehouses
Workflow orchestration and automation
Data quality, lineage, and observability
System scalability and reliability
Fundamentals of Data Engineering: Plan and Build Robust Data Systems (1st Edition) is an indispensable guide for anyone designing or maintaining large-scale data systems.
By blending technical depth with strategic clarity, Joe Reis and Matt Housley offer a roadmap for professionals who want to build data platforms that are scalable, efficient, and future-ready. Whether you’re modernizing legacy systems or constructing new cloud-native architectures, this book provides the expertise needed to engineer data with precision and impact.
