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Big Data
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  • Hadoop Explained (Aravind Shenoy)

    This book introduces you to Hadoop and to concepts such as 'MapReduce', 'Rack Awareness', 'Yarn' and 'HDFS Federation', which will help you get acquainted with the technology. It addresses the continuing challenge of Big Data with this insightful guide.

  • Big Data on Real-World Applications (Sebastian Ventura Soto)

    The aim of this book is to provide the reader with a variety of fields and systems where the analysis and management of Big Data are essential. It describes the importance of the Big Data era and how existing information systems are required to be adapted.

  • Concept of Scientific Inference When Working with Big Data

    Examine critical challenges and opportunities in performing scientific inference reliably when working with big data, focued on the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations.

  • Disruptive Possibilities: How Big Data Changes Everything

    This book takes you on a journey of discovery into the emerging world of big data, from its relatively simple technology to the ways it differs from cloud computing. It provides an historically-informed overview through a wide range of topics.

  • The Promise and Peril of Big Data (David Bollier)

    This book explores the positive aspects and the social perils that arise when the ever-rising floods of data being generated by mobile networking, cloud computing and other new technologies meets continued innovations in advanced correlation techniques.

  • O'Reilly® Big Data Now: Current Perspectives from O'Reilly Radar

    This book represents report recaps the trends, tools, applications, and forecasts. This collection of blog posts, authored by leading thinkers and experts in the field, reflects a unique set of themes we've identified as gaining significant attention and traction.

  • Understanding Big Data: Analytics for Hadoop and Streaming Data

    In this free book, the three defining characteristics of Big Data - volume, variety, and velocity, are discussed. Industry use cases are also included in this practical guide.

  • O'Reilly® Planning for Big Data: Changing Data Landscape

    This book provides an efficient, user-friendly 'brief' on the current status of Big Data analytics and how you can economically deploy this technology to increase your firm's profitability.

  • Hadoop Succinctly (Elton Stoneman)

    This booky explains how Hadoop works, what goes on in the cluster, demonstrates how to move data in and out of Hadoop, and how to query it efficiently. It also walks through a Java MapReduce example, illustrates it in Python and .NET, too.

  • Hadoop Illuminated (Mark Kerzner, et al)

    This book aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical. It book introduces you to Hadoop and to concepts such as 'MapReduce', etc., which will help you get acquainted with the technology.

  • BIG CPU, BIG DATA: Solving the World's Toughest Problems

    The goal of this book is to teach you how to write parallel programs that take full advantage of the vast processing power of modern multicore computers, compute clusters, and graphics processing unit (GPU) accelerators.

  • Data-Intensive Text Processing with MapReduce (Jimmy Lin)

    This free book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

  • O'Reilly® The Data Journalism Handbook (Jonathan Gray)

    This book is intended to be a useful resource for anyone who thinks that they might be interested in becoming a data journalist, or dabbling in data journalism.

  • Mining of Massive Datasets (A. Rajaraman, J. D. Ullman)

    This book teaches algorithms that have been used in practice to solve key problems in data mining and includes exercises suitable for students from the advanced undergraduate level and beyond.

  • O'Reilly® Agile Data: Building Data Analytics Applications

    How to create an environment for exploring data, using lightweight tools such as Ruby, Python, Apache Pig, and the D3.js (Data-Driven Documents) JavaScript library.

  • The Fourth Paradigm: Data-Intensive Scientific Discovery

    This book presents the first broad look at the rapidly emerging field of data-intensive science, with the goal of influencing the worldwide scientific and computing research communities and inspiring the next generation of scientists.