Few technical books earn the reputation of Designing Data-Intensive Applications. Since the first edition appeared in 2017, it has become required reading for backend developers, data engineers and anyone preparing for system design interviews. In March 2026, Martin Kleppmann and co-author Chris Riccomini released a long-awaited second edition.
It’s a big book (around 670 pages) and not a beginner’s book. So who should read it, and is it worth the effort? Here’s our review.
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Table of Contents
At a Glance
| Detail | Info |
|---|---|
| Authors | Martin Kleppmann and Chris Riccomini |
| Publisher | O’Reilly Media |
| Edition | 2nd (March 2026) |
| Length | Around 670 pages |
| Level | Intermediate to advanced |
| Verdict | The best book on how modern data systems really work |
What Is This Book About?
It’s not a book about one database. It’s about the ideas behind all of them: how data is stored, copied, split across machines, kept consistent and processed. Once you understand those ideas, choosing between PostgreSQL, MongoDB, Cassandra, Kafka or a cloud data warehouse stops being a guess, because you can see the trade-offs each one makes.
What the Book Covers
The book is built around three big themes:
- Foundations of data systems: what reliability, scalability and maintainability really mean; data models and query languages (relational, document and graph); how storage engines work, including B-trees and log-structured storage; and how data is encoded as it moves between services.
- Distributed data: replication, partitioning (sharding), transactions and isolation levels, the problems that appear when machines and networks fail, and how distributed systems reach agreement through consistency and consensus.
- Derived data: batch processing, stream processing, and how to combine many specialised systems into one reliable application.
The second edition updates the material for how systems are built today, including the major cloud services and the newer technologies that have appeared since 2017, while keeping the same focus on lasting principles.
What I Like
1. It explains trade-offs, not hype. Every technology is presented with its strengths and its costs. You learn to ask “what does this system give up to be fast?”, which is the most useful question in data architecture.
2. It connects to SQL concepts you already know. The chapters on storage engines and transactions explain what’s really happening behind indexes and isolation levels, which makes SQL Server or MySQL behaviour much easier to understand.
3. It’s beautifully written. Complex ideas like replication lag or consensus are explained with clear diagrams and everyday examples. It reads more like a well-told story than a manual.
4. It’s deeply referenced. Every chapter ends with a long list of papers and articles if you want to go further.
What Could Be Better
- It’s long and demanding. Expect to read it over weeks or months, not a weekend.
- There’s almost no code. It teaches concepts, not how to configure a particular product.
- It’s not for SQL beginners. If you’re still learning joins, this book will feel abstract.
Should You Upgrade From the First Edition?
If you already own and have read the first edition, the core ideas haven’t changed, so there’s no urgency. The second edition is worth it if you want the updated examples and the cloud-era perspective, or if your copy is worn out from re-reading. If you’re buying for the first time, get the second edition.
Should You Buy It?
Yes, if you’re a developer, data engineer or DBA who wants to understand why databases behave the way they do, or you’re preparing for system design interviews.
Not yet, if you’re new to SQL. Build your foundation first with our beginner SQL books, and come back when you’re comfortable designing and querying databases.
Our verdict: One of the most valuable technical books you can read. It will change how you think about every database you use.
Related Reading
On SimplifyDatabase, the ideas in this book connect to SQL transactions and ACID, SQL vs NoSQL, SQL indexes and data lakes vs data warehouses vs lakehouses.
For more books on data at scale, see our 5 best big data and data engineering books, or browse all book reviews under the SQL Books menu.