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

• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
• Martin Kleppmann
• Page: 500
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781449373320
• Publisher: O'Reilly Media, Incorporated

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Want to know how the best software engineers and architects structure their applications to make them scalable, reliable, and maintainable in the long term? This book examines the key principles, algorithms, and trade-offs of data systems, using the internals of various popular software packages and frameworks as examples. Tools at your disposal are evolving and demands on applications are increasing, but the principles behind them remain the same. You’ll learn how to determine what kind of tool is appropriate for which purpose, and how certain tools can be combined to form the foundation of a good application architecture. You’ll learn how to develop an intuition for what your systems are doing, so that you’re better able to track down any problems that arise.

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Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems Kleppmann Martin. ISBN: 9781449373320. Price: €  messaging | Data Intensive
A systematic way of approaching Big Data complex projects. In order to design a complete Data Ingestion Architecture and choose the right tools for it, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann (Apache Samza). Designing Data-Intensive Applications - Martin Kleppmann - Bok
Designing Data-Intensive Applications. The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. av Martin Kleppmann ( häftad , 2016). Bloggar (0). Hey, I'm writing a book! — Martin Kleppmann's blog
A book which would tell a story of the big ideas in data systems, the fundamental And so, Designing Data-Intensive Applications was born. Perhaps you want to work on highly scalable systems with millions of users, and practical constraints, about the reasoning behind every design decision. None of 

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