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

"O'Reilly Media, Inc."
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Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

  • Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
  • Make informed decisions by identifying the strengths and weaknesses of different tools
  • Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
  • Understand the distributed systems research upon which modern databases are built
  • Peek behind the scenes of major online services, and learn from their architectures
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About the author

Martin is a researcher in distributed systems at the University of Cambridge. Previously he was a software engineer and entrepreneur at Internet companies including LinkedIn and Rapportive, where he worked on large-scale data infrastructure. In the process he learned a few things the hard way, and he hopes this book will save you from repeating the same mistakes.



Martin is a regular conference speaker, blogger, and open source contributor. He believes that profound technical ideas should be accessible to everyone, and that deeper understanding will help us develop better software.

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Additional Information

Publisher
"O'Reilly Media, Inc."
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Published on
Mar 16, 2017
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Pages
614
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ISBN
9781491903100
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Language
English
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Genres
Computers / Data Modeling & Design
Computers / Desktop Applications / Databases
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Content Protection
This content is DRM free.
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Read Aloud
Available on Android devices
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Eligible for Family Library

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This is a practical guide for software developers, and different than other software architecture books. Here's why:

It teaches risk-driven architecting. There is no need for meticulous designs when risks are small, nor any excuse for sloppy designs when risks threaten your success. This book describes a way to do just enough architecture. It avoids the one-size-fits-all process tar pit with advice on how to tune your design effort based on the risks you face.

It democratizes architecture. This book seeks to make architecture relevant to all software developers. Developers need to understand how to use constraints as guiderails that ensure desired outcomes, and how seemingly small changes can affect a system's properties.

It cultivates declarative knowledge. There is a difference between being able to hit a ball and knowing why you are able to hit it, what psychologists refer to as procedural knowledge versus declarative knowledge. This book will make you more aware of what you have been doing and provide names for the concepts.

It emphasizes the engineering. This book focuses on the technical parts of software development and what developers do to ensure the system works not job titles or processes. It shows you how to build models and analyze architectures so that you can make principled design tradeoffs. It describes the techniques software designers use to reason about medium to large sized problems and points out where you can learn specialized techniques in more detail.

It provides practical advice. Software design decisions influence the architecture and vice versa. The approach in this book embraces drill-down/pop-up behavior by describing models that have various levels of abstraction, from architecture to data structure design.
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