In the four years that O'Reilly Media, Inc. has produced its annual Big Data Now report, the data field has grown from infancy into young adulthood. Data is now a leader in some fields and a driver of innovation in others, and companies that use data and analytics to drive decision-making are outperforming their peers. And while access to big data tools and techniques once required significant expertise, today many tools have improved and communities have formed to share best practices. Companies have also started to emphasize the importance of processes, culture, and people. The topics in represent the major forces currently shaping the data world: Big Data Now: 2014 Edition Cognitive augmentation: predictive APIs, graph analytics, and Network Science dashboards Intelligence matters: defining AI, modeling intelligence, deep learning, and "summoning the demon" Cheap sensors, fast networks, and distributed computing: stream processing, hardware data flows, and computing at the edge Data (science) pipelines: broadening the coverage of analytic pipelines with specialized tools Evolving marketplace of big data components: SSDs, Hadoop 2, Spark; and why datacenters need operating systems Design and social science: human-centered design, wearables and real-time communications, and wearable etiquette Building a data culture: moving from prediction to real-time adaptation; and why you need to become a data skeptic Perils of big data: data redlining, intrusive data analysis, and the state of big data ethics

Hadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere About the Technology About the Book It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date coverage of Hadoop available. Readers need to know a programming language like Java and have basic familiarity with Hadoop. What's Inside Thoroughly updated for Hadoop 2 How to write YARN applications Integrate real-time technologies like Storm, Impala, and Spark Predictive analytics using Mahout and RR About the Reader About the Author Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects. Quotes Very insightful. A deep dive into the Hadoop world. - Andrea Tarocchi, Red Hat, Inc. The most complete material on Hadoop and its ecosystem known to mankind! - Arthur Zubarev, Vital Insights Clear and concise, full of insights and highly applicable information. - Edward de Oliveira Ribeiro, DataStax, Inc. Comprehensive up-to-date coverage of Hadoop 2. - Muthusamy Manigandan, OzoneMedia

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.