Kleppmann M. Designing Data-Intensive Applications. The Big Ideas...2ed 2026

Textbook in PDF format 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. 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 Who Should Read This Book? If any of the following are true for you, you’ll find this book valuable: You’re a software engineer, software architect, or technical manager who needs to make decisions about the architecture of the systems you work on—for example, you need to choose tools for solving a given problem and figure out how best to apply them. This applies especially to backend systems. You’re a data engineer who wants to understand the wider context of the systems you deal with, or a cloud engineer who wants insights into the underpinnings of the systems you’re using. You will find that even though modern distributed systems hide a lot of complexity from you, understanding their underlying principles is extremely useful for performance optimization and debugging. You want to learn how to make data systems scalable (e.g., to support apps with millions of users), highly available (minimizing downtime), operationally robust, and easier to maintain in the long run (even as they grow and as requirements and technologies change). You are preparing for a “system design” job interview in which you will be asked to sketch an architecture for an application, and you need to learn the principles for good data architectures. You are curious to find out what goes on behind the scenes at major websites and online services, and inside various databases and data processing systems—especially if you like to dig deeper than buzzwords to gain a technically accurate and precise understanding of various technologies and their trade-offs. This book assumes that you already have some experience building web-based applications and that you are familiar with relational databases and SQL. A high-level understanding of common network protocols like TCP and HTTP is helpful. Your choice of programming language or framework makes no difference for this book. Contents: Preface Trade-Offs in Data Systems Architecture Defining Nonfunctional Requirements Data Models and Query Languages Storage and Retrieval Encoding and Evolution Replication Sharding Transactions The Trouble with Distributed Systems Consistency and Consensus Batch Processing Stream Processing A Philosophy of Streaming Systems Doing the Right Thing Glossary Index

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