Customer Reviews: Hadoop: The Definitive Guide
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on August 25, 2012
I had read all the positive reviews and really had high hopes for the book, waited for the 3rd edition thinking it would be current, but I've mainly felt frustration in reading it once past the first few chapters.

Reference to the Bible in other reviews are apt. The book is a mishmash of chapters with a wide variety of styles and intents. The writing giving the overview is great. But other chapters are a reference manual dump with little motivation. Other chapters tried to be guided tutorial, but lacked in important details (or were out dated by changes). Wish it could have been written with a clearer editorial point of view, or better organized in sections with similar purposes.

Keeping up with a such a fast moving project with a paperback book is no doubt a difficult task. I didn't feel the book did a good job of dealing with the changes that happened with the shift to 1.x .

Most frustrating were the mentions of the "book's website" as a source of up-to-date information. Which website? (,, Wouldn't it make sense to use a URL instead of the phrase "book's website?"

Minor complaint, don't like the code listings without filenames.

Expect to find a lot of time looking for stuff on the web that should have been included in the book or at least documented with a concrete URLs.

There are certainly example of truly fine technical writing in the book. Just wish that level could have been maintained through out the book.
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This book is the single best source to begin your career in Big Data Development. However this book should not be the first entry point, which will frustrate you. This review hopes to help the juniors and newbies, who want to enter the big data world.

Cloudera CCD-410 certification ranges between tough to very tough. Period.

TRAINING : You are not mandated to take a training. I took a relatively inexpensive training ($300) from edureka dot in, an online training website in India. They give a good overview at 10,000 feet are very good for the price,but no where close enough to get certified. Check out their first session available for free at Youtube. They do have steps to install your own VM, simple project , HIVE,PIG etc. If time and money permits, I strongly suggest going to official cloudera training. It costs about $3000 and includes a free test voucher , so effectively about $2700. Saves you months in preparation time and distinct advantage over your peers that should pay for itself.

Install VM, try few commands, PIG, hive commands, Also try Amazon elastic mapreduce which reduces lot of manual typing and allows you to focus on the coding itself.

LEARNING FROM THIS BOOK: After a training, start with this book. The first Eight chapters are critical (Approximately 300 out of 550 pages). If you are smart,sharp and young , expect to read these eight chapters about three times, more is just fine. Add some time to read rest of chapters once Or twice before the test and all the external links. If you are a busy professional, give a six month window to take the test. Knowing Java is a definitive plus. Buy the Cloudera mock examination after getting comfortable and familiar with Mapreduce($125). It is a nice resource. Explains every answer, links to where you can get more information . Just as an FYI, the real test was far more complex and difficult.

You will need to go through the example code, understand what each line does, why it is there, what happens if you comment out a line of the code. As an example,
return job.waitForCompletion(false) ? 0 : -1;

> What does waitForCompletion mean?,
> Is Reduce Job Must Or Optional ?
> How Many Files will running a Map job produce?
> Will the code compile or will it error at run time based on datatypes.?
> What will happen if you run the same job twice ?
> What happens to the map data after the job?
> How does Hadoop handle huge files that cross block boundaries ?
> What happens if you do not explicitly set a mapper or reducer ?
> Will a combiner help , based on a scenario ?
> Which daemon decides the number of Map job to run ?
> How does hadoop handle the blocks when a node crashes?

This is an extension of previous scenarios. A small table, a simple SQL query ( example : select stationid,max(temp) from tableX. Answer choice are four set of mapreduce code and you have to chose the right one. Expect to read and understand the mapreduce that emulates how you create a distinct, how you do a sum, average, max, min etc. According to Cloudera website, these are the percentage of questions.

CHAPTER 3 : 17 Percent
CHAPTER 4 : 6 Percent
CHAPTER 5 : 7 Percent
CHAPTER 6 : 18 Percent
CHAPTER 7 : 6 Percent
CHAPTER 8 : 7 Percent
PIG /HIVE/SQOOP/Zookeeper : 8 percent combined (no Hbase)

Chapter no 2 has no reference but is very important. Expect several questions from that chapter since it gives a good overview. Remaining is all the links that cloudera suggests to read and get familier. SQOOP import syntax, creating a hive table via sqoop , creating and populating hive table via sqoop are must knows.

I have heard the tiring argument that certification is purely academic. Tell that to your doctor or your Dentist. Sound fundamentals are the foundations behind real world experience. Big Data is no different. Understanding the basics will give the confidence; experience will follow while you keep your client happy.

My interest on Big Data was spooked by the Harvard Business Review Article claiming that "Data Scientist" was the hottest job of the 21st century. Follow that by googling for "Rayid Ghani", claimed as the data scientist behind Obama's second term victory.
hbr dot org forwardslash 2012 forwardslash 10 forwardslash data-scientist-the-sexiest-job-of-the-21st-century forwardslash ar forwardslash1

> Coursera provides a free course "Introduction To Data Science". I signed up for their first batch but could not finish with office commitments.
> Youtube for "Stanford University Hadoop" by Amr Awadallah

I was impressed with these books; You also might like them.
Big Data: A Revolution That Will Transform How We Live, Work and Think
Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
Data Science for Business: What you need to know about data mining and data-analytic thinking

Some day Big Data will become a commodity skillset,but not now. I did a search in glassdoor to see the demand for Hadoop vs some other hot ones. Hadoop is head and shoulders above the rest.
Hadoop - 30,011 postings on Apr 2014
Oracle DBA - 9227 postings ( A Perpetual hot skillset)
Salesforce - 9968 postings

Please post any questions in the comment section and I will certainly try to answer them.
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on September 19, 2012
I bought this book as a very experienced programmer but no prior experience with Hadoop, which I need to come up to speed on for a new project. I am extremely disappointed in the book and feel I wasted my money. If there's one thing you want from a book on a new technology, it's the ability to get a basic "Hello World" equivalent program running, from which you can then start iterating. This book completely falls down on this most basic requirement - when you get to the very first example program in the book, it tells you that you need to first compile a bunch of example code from the book's website. That shouldn't be required, but ok, whatever. Then when you go to the book's website, you are told that you first need to install a bunch of extra stuff covered later in the book before you can compile the libraries apparently needed to get anything at all to run. This really makes no sense at all - there's no way I should be having to read all the later chapters to figure out what these things are in order to get my very first example program running. Tossed it into the trash and off in search of a resource done by someone who understands how to structure a tutorial properly.
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on October 12, 2012
I purchased this book a few months ago based on many earlier 5-star reviews. I had high hopes that it would be as good as those reviewers highly praised. However, the book is actually unbelievably poorly organized - essentially written in a spaghetti fashion. Yes - it contains a lot of information about Hadoop, but with three basic issues: 1) examples are trivial and hard to get working due to insufficient, unclear or no procedures; 2) many subjects (e.g. streaming) are spread over several chapters and readers have to stitch them together after reading all relevant chapters; and 3) many stataments are either inaccurate or lack supportive data. Ironically, one has to apply MapReduce to all the subjects in order to sort out various subjects in a more logic order. I look forward to the 4th edition with significant quality improvement.
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on January 6, 2013
If you're looking to learn about what Hadoop is, all of the buzzwords/terms you've heard about (i.e. HDFS, MapReduce), and get an overview of software in the Hadoop ecosystem (Pig, Hive, etc.) this is a good book that will give you a good overview and pointers in the right direction.

However, the book isn't going to give you a lot of detail on programming MapReduce and things like that.

In other words, it's a good breadth book, not a good depth book. So YMWV depending on what you're looking for.

I bought the previous edition of this book and gave it 4 stars. I bought this newer edition looking for information about Hadoop 2.0, Yarn, and all of the new stuff coming out. It provided a little bit of information about this, but overall was lacking in these details. So I notched it down 1 star because of that. It was just too much duplicate information from the prior edition.
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on February 11, 2013
I bought this book for a project at work, to prototype a log analysis system using Hadoop. I haven't bought very many technical books in the last few years, but the quality of most online documentation for Hadoop is poor and books seemed like a better option. This book is considered the "bible" for Hadoop. It was useful, and I kept it open on my desk for quite a while as I worked to get the infrastructure set up. Consider it a high-level intro to lots of different Hadoop topics, and you'll be happy with it. Just don't expect it to answer all of your questions. You'll probably still end up doing a lot of digging through other online sources, because the Hadoop ecosystem is large and complicated, and no book can really cover all of it. Besides this book, I also bought Hadoop In Action (not quite as big as this book, but a useful counter-point) and Data Intensive Text Processing With MapReduce (which gave me a good intro to the Map Reduce algorithm, but wasn't that useful once I had a general idea what was going on).
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on March 5, 2014
This book is really difficult to understand on the first attempt. My husband and i went through many other tutorials before starting to read this one. Actually we tried learning hadoop starting from simple hadoop wiki page,,, yahoo materials, documents from authors like Jimmy Lin and Chris Dyer , free you tube tutorials and so on.

Then when we started learning from this book, we were able to understand the concepts quite vividly in the beginning 2 chapters yet we were crawling when we reached chapter 4 of Hadoop:Definitive Guide. We got really frustrated and stopped reading this book and decided not to continue it again. But later realizing that it is the very foundation of Hadoop we had to move on.So left with no other option we started with a different plan. This time we started with HIVE.It was quite an easy chapter to our surprise.Then we went on to HBASE. It was PIG that surprised us the most.Even though written by the same author who wrote Chapter 4, this one was pretty simple and illustrative.Now when we found that we were able to proceed through the chapters, we came back to Chapter 5 and then covered the rest of Map-Reduce.But still Chapter 4 is a Mystery....Had to skip it forever...But we found yahoo material explaining serialization pretty well...But couldn't deal with AVRO...Still searching for materials to learn that....:)
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on September 7, 2014
this is a jumbled up mess of things related to hadoop. the index is well organized and each chapter well named so that the reader feels that once he goes through the chapters he will "know" hadoop.

but in reality, the author starts talking about map reduce even before the map reduce chapter starts. the author assumes that the reader is an experienced java programmer or in some other language.

all things said its not one of those books that have a serial approach to learning a subject, starting from -10 and taking you to +100.
it starts with +23 and after taking lot of ups and downs, sometimes -20, sometimes +80, leaves you at +43. yes you have gained some knowledge but are you confident to talk about hadoop, definitely no. you just end up even more frustrated.
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on September 4, 2012
I read the book with attention mainly to Hadoop's underlying premises and platform architecture, and note that this review focuses on the book itself, not the subject of Hadoop in general.

Firstly, I agree with the reviewer noting the book's a "mishmash". It's rather unorganized and thus presented poorly in that it delivers a series of ad-hoc "how tos". After three editions, this should have been remedied.

But, what I feel is the largest shortcoming is that, while the author certainly seems to demonstrate deep knowledge of Hadoop and its related projects, he make numerous assertions of underlying platform concepts that are either unsubstantiated or completely incorrect. Given the complexities and efforts expected of large-scale, distributed systems, this is a critical weakness.

For example, page 3 under "Data Storage and Analytics" (and available under "Look Inside") illustrates a naïve and incorrect understanding of disk performance; research "understanding IOPS" to understand why this is. Ironically, actual and not theoretical performance would likely be worse than what he outlines so had he provided perhaps just a tad more accuracy, he would not only have maintained credibility, but also in turn made a stronger case for the limitations of disk I/O (albeit rotating in this context). This is not to split hairs since, and by his own statement, the focal point of Hadoop is mitigating mass storage and processing scalability bottlenecks, and Hadoop is the focal point of the book. Foundational knowledge, such as how to measure disk performance, in the problem itself is expected.

His knowledge of RAID concepts is also demonstrably quite lacking, and various RAID levels have to-date been the standard mechanism to speedup disk I/O and mitigate consequences of disk failure. HDFS has its own counterpart to RAID so a definitive guide to Hadoop must provide a definitive understanding of RAID. Again, this is squarely within the scope of the book so to expect the author to understand the topic is not unreasonable, but unfortunately here too his credibility suffers.

Page 3 also describes "how RAID works", but even that statement is inherently inaccurate. In practice, "RAID" itself isn't an absolute term and must be accompanied by a level, and certain levels serve completely different purposes (research "RAID levels"); his comment would be accurate rephrased as "how RAID 1 (mirroring) works". Later, in chapter 9, he does in fact refer to RAID 0, but then states that RAID 0 "is" (as opposed to "may be") slower than JBOD with HDFS. Regardless of whether than could be the case or not, it's presented as fact and, inexplicably, he offers a hyperlink to an email outlining a brief, one-off experiment as "proof". This is far from scientific or objective; to extrapolate a single cause from such an "experiment" is tantamount to junk science. The authors of the experiment's results themselves didn't even offer it as conclusive.

He also makes careless logical and mathematical generalizations, like in the following statement: "[i]n JBOD, disk operations are independent, so the average speed of operations is greater than that of the slowest disk.". That is not a true statement because if all of the disks are same speed (however that's measured...) then mean speed and each disk's speed would be equal. Furthermore, "[d]isk performance often shows considerable variation in practice, even for disks of the same model.". Period. End of story. No evidence, no citations, not even a logical proof. Nothing. A completely subjective and baseless assertion that the reader is expected to simply accept. This pattern unfortunately permeates the entire text.

His recommendation of JBOD, however, applies only to a certain class of Hadoop servers and for another he does in fact recommend RAID. Whether that reflects general consensus, I don't know, but after claiming that JBOD under HDFS outperforms RAID 0, he adds that JBOD is superior also because "if a disk fails in a JBOD configuration, HDFS can continue to operate without the failed disk, whereas with RAID, failure of a single disk causes the whole array (and hence the node) to become unavailable.". I'm sure that gave a chuckle to those who possess even the most basic understanding of RAID levels and level nesting. And besides the proposition being simply false at face value, it's also logically contradicts his suggestion a few paragraphs prior that RAID should be used, albeit for a certain server role, but used nevertheless. Whether he's gaming his RAID explanations to suit a particular purpose or he's playing fast-and-loose with terms he doesn't understand is unclear, but what is clear is that his information is unreliable.

Another example includes asserting a SAN impacts data center bandwidth. With virtually no exceptions, SANs are over dedicated fiber channels, not "the network", and thus "network bandwidth" potentially being a bottleneck, as he describes, is completely inapplicable.

He refers to a "1 GB" switch in several places and we're left to assume it's actually "1 Gbps". Similarly, references to "rational" rather than "relational" databases appear repeatedly early in the book. Misprints or not, they further erode credibility.
"Linear scalability" through parallel processing is a repeated reference, but at any scale--from multicore to thousand-node grids--engineers know that Amdahl's Law proves this is simply not possible. "Less non-linear" or a similar description would be accurate and not mislead the reader to believe doubling compute doubles speedup.

Ultimately, I'm disappointed in the extremely limited depth the author demonstrates in understanding distributed system and even simple computing fundamentals. Perhaps these topics have been rushed and perhaps other flaws are attributable to the publisher, but they are so central to the subject that to speak to them at all requires speaking to them intelligently and scientifically. Because the author unfortunately indicates little of either, I cannot recommend this book and will instead seek credibility on the subject elsewhere.
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on June 18, 2013
I feel like this book is very hard to follow and not organized well. This book is better as a reference book then a book to teach you about Hadoop. I had taken a Hadoop training course before reading this and I still had a difficult time following along. With that said, the combination of the training course and this book, I was able to pass the Cloudera Certfied Developer for Apache Hadoop (CCDH) certification exam. I gave this book 2 stars because I believe there was some key pieces of knowledge in this book that was asked on the exam.
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