Free Download Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
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Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Free Download Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
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Product details
Paperback: 624 pages
Publisher: O'Reilly Media; 1 edition (April 2, 2017)
Language: English
ISBN-10: 1449373321
ISBN-13: 978-1449373320
Product Dimensions:
7 x 1.2 x 9.2 inches
Shipping Weight: 2.2 pounds (View shipping rates and policies)
Average Customer Review:
4.8 out of 5 stars
141 customer reviews
Amazon Best Sellers Rank:
#1,663 in Books (See Top 100 in Books)
In Silicon Valley, "ability to code" is now the uber-metric to track. Starting from how engineers are interviewed, actual hands-on work (due to processes that overemphasizes "do" over "think, e.g., daily stand-ups require you to say what concrete thing you did yesterday), evaluation of work ("move fast and break things") to over-emphasizing on downstream "fixes" (prod-ops culture, 24*7 firefighting heroism) - the top echelon of technology gravitated towards things that it can see, feel, measure. What often gets neglected in this "code be all" culture is deep understanding of fundamental concepts, and how most newer "innovations" are indeed built on a handful time-honored principles.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!
I'm only 3 chapters into this book and I think it deserves a 5 star already.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
DDIA is easily one of the best tech books of 2017 (possibly this decade) and is destined to become a classic. The book deals with all the stuff that happens around data engineering : storage, models, structures, access patterns, encoding, replication, partitioning, distributed systems, batch & stream processing and the future of data systems (don't expect ML because it is a different beast).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. You’ll find that whenever you have a question after reading a particular sentence, the answer to that will be found in the upcoming sentences. It’s like mind-reading.Also kudos to the author for those nice diagrams and interesting maps (and 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.
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