Book Review: “Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems” by Martin Kleppmann

“Designing Data-Intensive Applications” by Martin Kleppmann is widely regarded as a cornerstone text for software engineers and architects working with modern data systems. Published in 2017, this book delves into the architectures of systems that are built to handle large volumes of data — including databases, stream processors, and web-scale applications. It is praised for its depth, clarity, and practical insights. Here’s a detailed review of this influential work.


The book is structured around three major themes: foundations of data systems, distributed systems challenges, and derived data. Martin Kleppmann explores a broad spectrum of topics such as storage, retrieval, encoding, replication, partitioning, batch processing, stream processing, and the intricacies of system design. Each chapter is rich with technical detail and real-world applications.

Content Quality

Kleppmann combines a deep understanding of theoretical concepts with extensive practical experience. This dual focus is evident in the way he presents complex topics with both rigor and practical relevance. The book is meticulously researched, with a plethora of references and pointers for further reading, making it not just a textbook but also a comprehensive guide to the current landscape of data systems.

Key Lessons and Features

  • Data Models and Query Languages: Understanding the trade-offs between different types of data models (relational, document, graph, etc.) and how they influence application development.
  • Storage and Retrieval: A look at various methods of storing data on disk and in memory, as well as challenges associated with indexing and search.
  • Scalability and Reliability: Strategies for designing systems that can scale out efficiently and maintain high availability and durability.
  • Consistency and Consensus: Deep dives into the problems of data consistency, concurrency control, and achieving consensus across distributed systems.
  • Stream Processing: Insights into processing data in real-time, handling stream data, and designing systems for event sourcing.


While the book is technical, its real-world examples and case studies make the abstract concepts tangible and relatable. Kleppmann discusses tools and technologies currently in use, providing a snapshot of best practices as well as historical context. This approach helps readers understand not only how to use these systems but also why certain designs or technologies might be chosen over others.


“Designing Data-Intensive Applications” is suited for software developers and architects who design and build systems handling large amounts of data. It requires a basic understanding of software development and databases; thus, it might not be the best fit for absolute beginners but is invaluable for intermediates and experts in the field.


“Designing Data-Intensive Applications” is a must-read for professionals dealing with the challenges of modern data systems. Its thorough analysis and detailed discussion make it an essential resource for understanding and building complex data-intensive applications. The book is not only a guide but also a thought leader in the design of scalable and reliable systems.

Overall, Martin Kleppmann’s work is an exemplary blend of theory and practice, highly recommended for anyone involved in the design, development, or management of data-intensive applications. It’s a book that readers will likely return to repeatedly as a reference throughout their careers.

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