Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable and Maintainable Systems

Kleppmann, Martin; Riccomini, Chris

ISBN 10: 1098119061 / ISBN 13: 9781098119065

Published by O'Reilly Media, 2026

About this Item

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Bibliographic Details

• Title: Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable and Maintainable Systems
• Publisher: O'Reilly Media
• Publication Date: 2026
• Language: English
• ISBN 10: 1098119061
• ISBN 13: 9781098119065
• Binding: Soft cover
• Edition: 2nd Edition
• Condition: good

About this title

Synopsis

Data is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of systems, including relational databases, NoSQL datastores, data warehouses, and data lakes. There are cloud services, on-premises services, and embedded databases. What are the right choices for your application? How do you make sense of all these buzzwords?

In this second edition, authors Martin Kleppmann and Chris Riccomini build on the foundation laid in the acclaimed first edition, integrating new technologies and emerging trends. You'll be guided through the maze of decisions and trade-offs involved in building a modern data system, learn how to choose the right tools for your needs, and understand the fundamentals of distributed systems.

• Peer under the hood of the systems you already use, and learn to use them more effectively
• Make informed decisions by identifying the strengths and weaknesses of different tools
• Learn how major cloud services are designed for scalability, fault tolerance, and consistency
• Understand the core principles upon which modern databases are built

About the Author

Martin Kleppmann is an associate professor at the University of Cambridge, researching and teaching distributed systems and security protocols. He co-created Automerge, was a startup founder, and a software engineer at LinkedIn, where he implemented large-scale data systems.