Hadoop – The Definitive Guide 4e: Storage and Analysis at Internet Scale

THE definitive guide on Hadoop. Theres a reason its pretty much the standard on Hadoop at this point. Not sure if there is a 5th edition out soon, so investigate that soon before buying this.

It helpful for my career growth

arrived on time. product was as advertised.

Great book. Found it invaluible when learning about Hadoop

Excellent book to learn Hadoop.

delivered as expected, excellent

Hi, I have written a detailed chapter-by-chapter review of this book on www DOT i-programmer DOT info, the first and last parts of this review are given here. For my review of all chapters, search i-programmer DOT info for STIRK together with the book's title. This very popular Hadoop book reaches its fourth edition, how does it fare? The creation of ever increasing amounts of data has changed the way data is being processed. Hadoop is the most popular platform for processing this big data. This updated book covers Hadoop 2 exclusively, with new chapters on several of Hadoop’s components. This is a wide ranging book divided into five parts. It cover Hadoop’s core components, Hadoop installation and maintenance, various Hadoop-related projects, and some case studies, spread over twenty four chapters. This book is aimed at developers, architects, and administrators. Some experience of Java or equivalent is needed to get the most out of the book. Below is a chapter-by-chapter exploration of the topics covered. Part I Hadoop Fundamentals Chapter 1 Meet Hadoop This chapter opens with a look at the recent explosion in data volumes. It is estimated that digital data occupied 4.4 zettabytes in 2013 (a zettabyte is a billion terabytes), and this is expected to grow to 44 zettabytes by 2020. Big data systems have been created to process this data in a timely manner. Hadoop is the most popular big data platform. The Hadoop Distributed File System (HDFS) allows files to be split, distributed, and duplicated over the servers, this give resilience and allows parallel processing. MapReduce is the processing model used for distributed batch processing. The earlier version of Hadoop had some problems, many of these have been solved with the implementation of Yet Another Resource Negotiator (YARN), which is responsible for cluster resource management. Additionally, YARN allows other processing models, in addition to MapReduce, to run. A useful overview of the history of Hadoop is provided, covering the milestones from its beginnings as Nutch in 2002, to its award winning data processing benchmarks. The chapter ends with a brief look at what’s in the rest of the book. This chapter provides a useful overview of how the need for distributed processing arose, and how Hadoop fulfils these needs. The core components of Hadoop (HDFS, MapReduce, and YARN) are outlined. It’s noted that increasingly, Hadoop is taken to mean both the core components and various related components. The prose, explanations, examples, and diagrams are well written, relevant and helpful – as they are throughout the book. . . . Conclusion This book covers a broad range of Hadoop topics, including Hadoop core (HDFS, MapReduce, and YARN) and many related components, additionally, installation details and case studies are included. The book is well written, providing good explanations, examples, walkthroughs, and helpful diagrams. Useful links are given between chapters and to websites. Most chapters have footnotes and a “further reading” section so you can obtain more information. You probably need an understanding of Java or a similar language to get the most out of the book. It should take your general level of understanding from level 3 to level 8. Since the book covers internals, administration, and development, I’m not sure who will read the entire book. Some sections seemed dry on first reading. Some of the books that are referenced are getting old. Not all components are covered (e.g. Storm), but many popular ones are. I did wonder if there was too much emphasis on MapReduce, since there seems to be movement away from MapReduce batch processing towards interactive processing, as shown with the growing popularity of Spark. Despite these minor criticisms, if you want to gain a good understanding of the current state of Hadoop and its components, I can highly recommend this book.

I spent a few hours studying a colleagues copy and decided not to buy. It is a perfectly good book, but it is way too long. As a software developer and IT consultant I have to learn or stay up-to-date with dozens of technologies and programming languages. I just do not have time to work through 600+ plus pages on every one. The background information on who did what when, who is friends with who is all very interesting, but I do not need to know any of that to learn to use the software. I understand that the creators are proud of their work, and can become expansivve about it, but for those of us working in IT someone's life's work is just one more technology or tool amongst dozens to be mastered (or at least understood well enough to work with). It is also a problem that a long book tends to be a heavy book, and that makes it diffficult to carry very much reference material around with me. [I know I can get electronic versions, but a paper version is still easier (at least for me) to learn from, and I see little point in buying both]. This is probably a perfect book for the Hadoop specialist, but it misses the mark for a multi-skilled generalist. All I want to see is a con concise overview of what a component does, a similarly concise description of it architecture, definitions of the APIs, and a few terse (but well explained examples).

This book is a good reference for the Hadoop ecosystem with a lot of code examples. It goes into details of the Map Reduce paradigm and practical topics of how to set a cluster. It also have dedicated chapters for the key tools that compose the Hadoop ecosystem like Scoop, flume, Pig, Hive, HBase. Many of the code examples use Java (since Hadoop is built in java). I'm not familiar with java but I could get the jist of the explanations just fine. The code is very well explained. It's a great read!

Me ha llegado en perfecto estado. Un libro imprescindible para conocer Hadoop a fondo y Amazon la unica web donde se puede encontrar en españa.

Excelente referencia para quienes inician con el tema de Big Data y Hadoop. Para gente técnica interesada en saber cómo hacer las cosas.

I have around 14 years of java experience and this was my first book ever on Hadoop. So far I have been reading from internet. I agree that at times it's hard to understand things in one shot but once you re read them, they get clear. The best thing about this book is that it covers everything in new API. I believe Tom White worked really hard to make the overall picture easy enough to understand. I would suggest one thing while reading this book, please have the computer and some IDE open while reading it otherwise you will get lost in theory (which is great nonetheless) and when you actually try to code later, things will go off the top of your head. Another thing is that, there are various things which you'll understand only after reaching at 4th or 5th chapter. If you are learning Hadoop (from scratch) and you have read its first few chapters only, you may find it difficult but once you reach at 4th or 5th chapters then I'm sure you will find this book amazing.

Excellent book lots of detailed on configuration. Configuration examples according to cluster size (which is difficult to find in books). Will recommend to anyone from beginner to advanced as most used tools are explained in deep