Ideal for students and professionals preparing for system design interviews and seeking to deepen understanding of data engineering concepts.
✅ Highly respected software engineering reference covering scalable systems, databases, distributed systems, and modern application design. ✅ Pages are crisp, clean, and untouched. The photos represent the condition and quality of our inventory.
O’Reilly Designing Data-Intensive Applications 1st Ed Martin Kleppmann 2017 PB
Genre/Topics: distributed systems, database design, data-intensive applications, big data infrastructure, scalability, reliability, consistency, fault tolerance, streaming and batch processing, and modern data engineering for software systems.
Only includes what is pictured. See photos for condition.
“Designing Data-Intensive Applications” by Martin Kleppmann is a comprehensive textbook focusing on the principles of designing reliable, scalable, and maintainable systems. Published by O’Reilly Media, this first edition book is aimed at adult and further education levels, with an advanced level of content. The book covers data modeling and design, as well as desktop applications and databases, making it a valuable resource for anyone looking to enhance their understanding of data-intensive systems. With 614 pages and a focus on big ideas behind data-intensive applications, this textbook is a must-read for those interested in the field of computers and data management.
O’Reilly Designing Data-Intensive Applications Jigsaw Puzzle.
Written byMartin Kleppmann, this highly respected book breaks down complex data systems and explains how modern applications handle data at scale, including databases, streaming systems, and distributed architectures.
By Martin Kleppmann & Chris Riccomini. Designing Data-Intensive Applications — 2nd Edition. Batch and stream processing. Whether you are asoftware engineer, backend developer, data engineer, architect, or computer science student, this book is an excellent reference for understanding the big ideas behind modern distributed and data-intensive applications.