Download Data Analytics with Hadoop: An Introduction for Data Scientists
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Data Analytics with Hadoop: An Introduction for Data Scientists
Download Data Analytics with Hadoop: An Introduction for Data Scientists
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Product details
Paperback: 288 pages
Publisher: O'Reilly Media; 1 edition (June 18, 2016)
Language: English
ISBN-10: 9781491913703
ISBN-13: 978-1491913703
ASIN: 1491913703
Product Dimensions:
6.8 x 0.6 x 9.2 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#412,332 in Books (See Top 100 in Books)
Be prepared to spend a lot of time debugging their sample code and the installation instructions for the various software packages. Most of the examples in the book do not match the sample code or data provided. And much of what they refer to in the book is not included in the samples. On top of all this, they even manage to mangle their examples just within the book itself. As an example, they walk through a data exercise first using mapreduce. Then they try to do the same thing using spark. But the spark program produces incorrect results. Yes, it works, but it isn't what mapreduce produced, or the book claimed was the expected output.On the plus side, the best way to learn something is by doing and this book will give you plenty of opportunities to figure things out on your own. That's always a plus. But not for the authors.
Best buy!
Good book to start Big Data with.
I really like this book. It is a great overview of a plethora of topics around doing scalable data analytics and data science. It is extremely up-to date, going through techniques that have existed for many years now like MapReduce, but also newer systems like Spark, all in the context of the Hadoop eco-system. They go into machine learning techniques, data management, and overall paint a nice picture around what data science is, and why data products are important, while teaching you how to make them!Every single concept is explained in a clear and concise manner, and wherever details are omitted there is always a citation to a source where the reader can continue reading more about it, which I think is great. Although I wouldn’t classify myself as a beginner, I believe it is friendly to both professionals and beginners, as it is centered around python which makes most examples (that are conveniently uploaded in a nice github repository) really easy to simply run and play around with. After describing something, whether that would be a technique for data analysis, or just the in-and outer workings of some analysis platform like HBase, Hive etc, the authors provide examples so that while you’re reading about this stuff you can also run it, play around with it and really explore how these systems function; I believe this is a crucial part of familiarizing ones’ self with new platforms.Another thing I enjoyed a lot was the ending of this book. After you really dive into all of these systems and get your feet wet with each one of them, the authors wrap it all up in a nice bow by taking a step back and describing the entire end-to-end process of how you would go about productively using the knowledge you’ve gotten from this book to build data analytics workflows!I highly recommend this to anyone who both knows that they want to learn how to deploy scalable analytics workflows in 2016, but also to readers who are simply just curious about data science; this book will suck you in!
Really nice textbook to work and learn hadoop systems and Mapreduce.
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