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Data Analytics with Hadoop: An Introduction for Data Scientists
Ebook Free Data Analytics with Hadoop: An Introduction for Data Scientists
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About the Author
Benjamin Bengfort is a Data Scientist who lives inside the beltway but ignores politics (the normal business of DC) favoring technology instead. He is currently working to finish his PhD at the University of Maryland where he studies machine learning and distributed computing. His lab does have robots (though this field of study is not one he favors) and, much to his chagrin, they seem to constantly arm said robots with knives and tools; presumably to pursue culinary accolades. Having seen a robot attempt to slice a tomato, Benjamin prefers his own adventures in the kitchen where he specializes in fusion French and Guyanese cuisine as well as BBQ of all types. A professional programmer by trade, a Data Scientist by vocation, Benjamin's writing pursues a diverse range of subjects from Natural Language Processing, to Data Science with Python to analytics with Hadoop and Spark.Jenny Kim is an experienced big data engineer who works in both commercial software efforts as well as in academia. She has significant experience in working with large scale data, machine learning, and Hadoop implementations in production and research environments. Jenny (with Benjamin Bengfort) previously built a large scale recommender system that used a web crawler to gather ontological information about apparel products and produce recommendations from transactions. Currently, she is working with the Hue team at Cloudera, to help build intuitive interfaces for analyzing big data with Hadoop.
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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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