Hadoop Beginner's Guide: Get your mountain of data under control with Hadoop. This guide requires no prior knowledge of the software or cloud services – just a willingness to learn the basics from this practical step-by-step tutorial.

I am very interested on Big Data and have read a lot of books about the subject. It was time for me to get hands. So I was looking for a beginner book for Hadoop. I already had the knowledge on Big Data as a subject and I also knew the type of problems we are solving. So I looked for a beginner book for Hadoop as I didn't have any prior experience with the platform. I am using Mac OSX machine. I know my way around linux/Unix shell.I started working through the book. The first chapter is a great short and sweet introduction to Hadoop, MapReduce and HDFS. Its not too lengthy. Just the right length for a person like me who gets bored pretty quickly with a lot of information. From Chapter 2, the real hands on work started. Although the instructions are mainly for Ubuntu linux, it worked like a charm in Mac. I didnt have any issues that other reviewers mentioned. when you run the first example, i couldn't use the exact command given in the book to run the first example of Pi calculation simply because i was using the latest version of Hadoop so the Jar name was different. Thats not something that the author can fix. once you give the correct jar name, it worked like a charm. Im now moving alone to the other chapters very quickly. Its a really good book if you just wanna familiarize yourself with the product, which is exactly the book is about. its well written. easy to follow. Author doesnt try to bore you to death.I recommend reading the below book if you first wanna understand what the Big Data is all about about and see what type of issues its trying solvehttp://www.amazon.com/gp/product/B009N08NKW/ref=cm_cr_ryp_prd_ttl_sol_39

A very good book for the beginner

I like this book, it is very clear and has good examples to try out. I don't give it 5 stars because many of the examples have mistakes, many of them very obvious.

Very good for beginners. Gives both the big picture, as well as the start up implementation for newbies. Execellent choice.

I read this book "out of context", meaning that I didn't have an interesting problem solvable by MapReduce at hand and a dire need to learn Hadoop at the time of reading. Instead, I took time to read this book with the purpose of determining whether it's a good beginner book or not. All in all, I'd say that it is. The author really succeeds in creating a context for Hadoop and its ecosystem.From the second chapter and onwards, Hadoop is gradually introduced using very detailed instructions. The general format for doing this is by listing every single command the user needs to type and its output, so the book is full of terminal session listings. All such listings are followed by sections called "What just happened?" that explain in detail the purpose of the commands and their output. This is actually quite helpful for readers who understand what's happening from just looking at the session listing; such readers can safely skip these sections.The above approach should enable any reader, regardless of level of experience, to follow along and do the exercises or labs, which is a good thing for a beginner book. I have a remark about this though: the session dumps could have been proofread better! I can't say that I read them through a magnifying glass, but still I found quite a few errors.As for the contents, the book never shows the monster! In my opinion, the introductory chapter fails to actually establish a case for Hadoop and MapReduce. Yes, it's about big data, scaling and problems and so on, but I couldn't find a logical transition to Hadoop as a solution to these problems. Instead, chapter two illustrates the framework with a distributed calculation of pi and the word counting program (Hadoop's version of the "Hello world" program).In a later chapter, Hadoop is used to process a dataset with UFO sightings, and then, in a chapter on advanced techniques a graph problem is solved. Not until that chapter did I start getting a feeling for what kind of problems Hadoop and MapReduce should be used for. This is what I mean by "never showing the monster". Being an introductory text, I'd prefer the first or second chapter to describe some problems that are good candidates for the MapReduce paradigm, illustrate one of them, and then show how a distributed computation would help.That said, I may be off track here. This is a book on Hadoop, and not MapReduce in general, and I did say that I read it without having intricate MapReduce problems at hand. This is pretty much my only criticism. If a reader doesn't perceive this as a problem, then there's nothing to complain about. After reading the book, I feel that I have a very good feeling for what Hadoop does and what building blocks in its ecosystem to use.One more thing... Here and there the book contains examples of how to use Amazon's EMR. This didn't feel awfully important to me, but it provides an even more solid explanation of how to apply the framework to bigger problems and how it can be used in a cloud environment.To sum up: a good and comprehensive book on Hadoop that covers the framework and its ecosystem, verbose and easy to follow examples, and a structure that leaves the reader with a sense of getting the big picture.Minuses: could devote some more pages to the MapReduce paradigm and have its sample listings proofread better.