- Tapa blanda: 400 páginas
- Editor: O'Reilly Media; Edición: 1 (25 de enero de 2016)
- Idioma: Inglés
- ISBN-10: 1449373321
- ISBN-13: 978-1449373320
- Valoración media de los clientes: 4 opiniones de clientes
Clasificación en los más vendidos de Amazon:
nº10.555 en Libros en idiomas extranjeros (Ver el Top 100 en Libros en idiomas extranjeros)
- n.° 24 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Bases de datos
- n.° 25 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Software y aplicaciones de negocio
- n.° 127 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Ciencias informáticas
Compara precios en Amazon
+ EUR 2,99 de gastos de envío
+ Envío GRATIS
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (Inglés) Tapa blanda – 25 ene 2016
|Nuevo desde||Usado desde|
Comprados juntos habitualmente
Los clientes que compraron este producto también compraron
Descripción del producto
Reseña del editor
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.
Biografía del autor
Martin Kleppmann is a software engineer and entrepreneur. He has co-founded two startups including Rapportive, which was acquired by LinkedIn. At these companies he worked on various data infrastructure systems, and learnt a few things the hard way. He hopes that this book will save you from repeating the same mistakes.
Martin enjoys figuring out complex problems and breaking them down, making them simple and accessible. He does this in his conference talks, on his blog at http://martin.kleppmann.com and by contributing to open source projects such as Apache Samza. You can find him as @martinkl on Twitter.
No es necesario ningún dispositivo Kindle. Descárgate una de las apps de Kindle gratuitas para comenzar a leer libros Kindle en tu smartphone, tablet u ordenador.
Obtén la app gratuita:
Detalles del producto
Si eres el vendedor de este producto, ¿te gustaría sugerir ciertos cambios a través del servicio de atención al vendedor?
Los clientes que vieron este producto también vieron
4 opiniones de clientes
Valorar este producto
Mostrando 1-4 de 4 opiniones
Ha surgido un problema al filtrar las opiniones justo en este momento. Vuelva a intentarlo en otro momento.
The book is quite theorical, but it's so well explained that you understand almost all concepts even if you have never heard of them.
In my opinion, it's a must for anyone who wants to work with data intensive applications (actually, it's a must for anyone interested in computer science).
Hace digeribles los conceptos más avanzados (desde un punto de vista de la industria actual), sin dejar de entrar en el detalle que requiere un profesional de estas disciplinas.
Opiniones de clientes más útiles en Amazon.com
Nowhere else perhaps is this more prominent than in data space that up-levels libraries and frameworks as the conversation starter. That gets in the way of success. It is indeed impossible to model Cassandra "tables" without understanding - at least - quorum, compaction, log-merge data structure. Due to the way the present day solutions are built ("fits one use case perfectly well"), if these solutions are not implemented well to the particular domain, failure is just a release away.
Mr Kleppmann does a great job of articulating the "systems" aspects of data engineering. He starts from a functional 4 lines code to build a database to the way how one can interpret and implement concurrency, serializability, isolation and linearizability (the latter for distributed systems). His book also has over 800 pointers to state of the art research as well as some of the computer science's classic papers. The book slows down its pace on the chapter on Distributed System and on the final one. A good editor could have trimmed about 120 pages and still retain most value one could get from the book.
That said, if you ever worked on data systems, especially across paradigms (IMS -> RDBMS -> NoSQL -> Map-Reduce -> Spark -> Streaming -> Polyglot), this book is pretty much only resource out there to tie the "loose ends" and paint a coherent narrative. Highly recommended!
If you are interested in distributed systems or scalability, this book is a must-read for you. It gives you a high level understanding of different technology, including the idea behind it, the pros and cons, and the problem it is trying to solve. A great book for practitioners who want to learn all the essential concepts quickly.
I didn't come from a traditional CS background, but I did have some basic knowledge in hardware and data structure. You will need some of that, such as hard disk vs SSD and AVL tree, to understand the materials. If you are completely new to backend or DS, you may want to start with another book "Web Scalability for Startup Engineers." After that book, you can read the free article "Distributed Systems for Fun and Profit" and you are good to go for this amazing book :D
Kleppman has coherently blended the relevant computer science theory with modern use cases and applications.The focus is primarily on the core principles and thought-processes that one must apply when it comes to building data services. Design concepts don't go out-of-date soon, so the book has very long shelf-life.
The high-point of this book is the author's lucid prose, which indicates mastery of the subject matter and clarity of thought. Conceptualizing reality is an art and the author really shines here. Kudos for those understandable diagrams and interesting maps (and also for avoiding mathematical formulas with Greek symbols). The bibliography at the end of each chapter is thorough enough for unending personal research.
If you are working on or interviewing for big data engineering, systems design, cloud consulting or devops/SRE, then this book is a keeper for a long-long time.