DasGupta - Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

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Nataraj Dasgupta is the vice president of Advanced Analytics at RxDataScience Inc. Nataraj has been in the IT industry for more than 19 years and has worked in the technical and analytics divisions of Philip Morris, IBM, UBS Investment Bank and Purdue Pharma. He led the data science division at Purdue Pharma L.P. where he developed the companys award-winning big data and machine learning platform. Prior to Purdue, at UBS, he held the role of associate director working with high frequency and algorithmic trading technologies in the Foreign Exchange trading division of the bank.

Giancarlo Zaccone has more than 10 years experience in managing research projects both in scientific and industrial areas. He worked as a researcher at the C.N.R, the National Research Council, where he was involved in projects on parallel numerical computing and scientific visualization.
He is a senior software engineer at a consulting company, developing and testing software systems for space and defense applications.
He holds a master's degree in physics from the Federico II of Naples and a second level postgraduate master course in scientific computing from La Sapienza of Rome.

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This book introduces the reader to a broad spectrum of topics related to big data as used in the enterprise. Big data is a vast area that encompasses elements of technology, statistics, visualization, business intelligence, and many other related disciplines. To get true value from data that oftentimes remains inaccessible, either due to volume or technical limitations, companies must leverage proper tools both at the software as well as the hardware level.

To that end, the book not only covers the theoretical and practical aspects of big data, but also supplements the information with high-level topics such as the use of big data in the enterprise, big data and data science initiatives and key considerations such as resources, hardware/software stack and other related topics. Such discussions would be useful for IT departments in organizations that are planning to implement or upgrade the organizational big data and/or data science platform.

The book focuses on three primary areas:

1. Data mining on large-scale datasets

Big data is ubiquitous today, just as the term data warehouse was omnipresent not too long ago. There are a myriad of solutions in the industry. In particular, Hadoop and products in the Hadoop ecosystem have become both popular and increasingly common in the enterprise. Further, more recent innovations such as Apache Spark have also found a permanent presence in the enterprise - Hadoop clients, realizing that they may not need the complexity of the Hadoop framework have shifted to Spark in large numbers. Finally, NoSQL solutions, such as MongoDB, Redis, Cassandra and commercial solutions such as Teradata, Vertica and kdb+ have provided have taken the place of more conventional database systems.

This book will cover these areas with a fair degree of depth. Hadoop and related products such as Hive, HBase, Pig Latin and others have been covered. We have also covered Spark and explained key concepts in Spark such as Actions and Transformations. NoSQL solutions such as MongoDB and KDB+ have also been covered to a fair extent and hands-on tutorials have also been provided.