Principles of Big Data
Preparing, Sharing, and Analyzing Complex Information
By Jules J Berman
Publisher: Elsevier / Morgan Kaufmann
Final Release Date: May 2013
Pages: 288

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.



• Learn general methods for specifying Big Data in a way that is understandable to humans and to computers.

• Avoid the pitfalls in Big Data design and analysis.

• Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources.

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5.0

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5.0

Fundamental, full of wisdom and advice

By ArthurZ

from Toronto, Ontario

About Me Developer

Verified Reviewer

Pros

  • Accurate
  • Thorough
  • Well-written

Cons

    Best Uses

    • Intermediate
    • Novice

    Comments about oreilly Principles of Big Data:

    A fantastic book! Must be part, if not yet, of the fundamentals of the Big Data as a field of science.

    Highly recommend to those who are into the Big Data practice.

    Yet, I confess this book is one of my best reads this year and for a number of reasons:

    The book is full of wisdom, intimate insight, historical facts and real life examples to how Big Data projects get conceived, operate and sadly, yes, sometimes die.

    But not only that, the book is most importantly is filled with valuable advice, accurate and even overwhelming amount of reference (from the positive side), and the author does not event stop there:

    there are numerous technical excerpts, links and examples allowing to quickly accomplish many daunting tasks or make you aware of what one needs to perform as a data practitioner (excuse my use of the word practitioner, I just did not find a better substitute to it to trying to reference all who face Big Data).

    Be aware that Jules Berman's background is in medicine, naturally, this book discusses this subject a lot as it is very dear to the author's heart I believe, this does not make this book any less significant however, quite the opposite, I trust if there is an area in science or practice where the biggest benefits can be ripped from Big Data projects it is indeed the medical science, let's make Cancer history!

    On a personal note, for me as a database, BI professional it has helped to understand better the motives behind Big Data initiatives, their underwater rivers and high altitude winds that divert or propel them forward.

    Additionally, I was impressed by the depth and number of mining algorithms covered in it. I must tell this made me very curious and tempting to find out more about these indispensible attributes of Big Data so sure I will be trying stretching my wallet to acquire several books that go more in depth on several most popular of them.

    My favorite parts of the book, well all of them actually, but especially chapter 9: Analysis, it is just very close to my heart. But the real reason is it let me see what I do with data from a different angle. And then the next - "Special Considerations", they are just two logical parts.

    The writing language is of this book is very acceptable for all levels, I had no technical problem reading it in ebook format on my 8" tablet or a large screen monitor.

    If I would be asked to say at least soemthing negative I have to state I had a feeling initially that the book's first part reads like an academic material relaxing the reader as the book progresses forward.

    I admit I am impressed with Jules' abilities to use several programming languages and OSS tools, bravo! And I agree, it is not too, too hard to grasp at least the principals of a modern programming language, which seems becomes a defacto knowledge standard item for any modern human being.

    So grab a copy of this book, read it end to end and make yourself shielded from making mistakes at any stage of your Big Data initiative, by the way this book also helps build better future Big Data projects.

    Disclaimer: I received a free electronic copy of this book as part of the O'Reilly Blogger Program

    (3 of 4 customers found this review helpful)

     
    5.0

    Awesome book

    By Fabio Alessandro Locati

    from Milan, Italy

    About Me Developer, Sys Admin

    Verified Reviewer

    Pros

    • Accurate
    • Easy to understand
    • Helpful examples

    Cons

      Best Uses

      • Intermediate
      • Novice
      • Student

      Comments about oreilly Principles of Big Data:

      As you probably know, I usually do not comment the books layout, being more focused on the contents of a book instead of it's layout.

      This time I will start this review speaking about the layout of this book since it's REALLY peculiar. The Introduction is written in a two-columns layout, very similar to the Science magazine layout. The book itself is written in a single column layout, still with a "Science like" look. What shocked me a little bit about this is the sense of old scientific document that this book has.This is true until you read the first few pages. As soon as you do it, the sense of old scientific document disappears quickly.

      The book is well written and the author explains a lot of things on Big Data. None of them is technical (ie: how to install Hadoop or how to extract data from a Big Data system). This is a feature of this book since it has no pretense to teach to IT people how to do their job, while tries to explain to the anyone who is somehow related to a Big Data system (ie: administrator, user, lawyer, manager) how it works.

      The only downside I saw on this book is that often the examples are related to hospitals and more specifically to pathologists. The reason of this is obvious if you read the author biography. Even if after few examples about these themes you start wondering if you'll find some different examples in the entire book, the examples are very easy to be transported to any other field in which Big Data makes sense.

      I think that this book is unique thanks to it's approach and a very good reading to anyone that wants to understand deeply the Big Data.

      Disclaimer: I received a free electronic copy of this book as part of the O'Reilly Blogger Program

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