Hadoop

Apache Flume: Distributed Log Collection for Hadoop - Second Edition 2nd Edition

Apache Flume is a distributed, reliable, and available service used to efficiently collect, aggregate, and move large amounts of log data. It is used to stream logs from application servers to HDFS for ad hoc analysis. This book starts with an architectural overview of Flume and its logical components. It explores channels, sinks, and sink processors, followed by sources and channels. By the end of this book, you will be fully equipped to construct a series of Flume agents to dynamically transport your stream data and logs from your systems into Hadoop.

Hadoop MapReduce v2 Cookbook Second Edition 2nd Edition

Starting with installing Hadoop YARN, MapReduce, HDFS, and other Hadoop ecosystem components, with this book, you will soon learn about many exciting topics such as MapReduce patterns, using Hadoop to solve analytics, classifications, online marketing, recommendations, and data indexing and searching. You will learn how to take advantage of Hadoop ecosystem projects including Hive, HBase, Pig, Mahout, Nutch, and Giraph and be introduced to deploying in cloud environments.

Pro Hadoop Data Analytics: Designing and Building Big Data Systems using the Hadoop Ecosystem 1st ed. Edition

Learn advanced analytical techniques and leverage existing toolkits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems which go beyond the basics of classification, clustering, and recommendation.

Hadoop Beginners Guide

Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night. Hadoop can help you tame the data beast. Effective use of Hadoop however requires a mixture of programming, design, and system administration skills. "Hadoop Beginner's Guide" removes the mystery from Hadoop, presenting Hadoop and related technologies with a focus on building working systems and getting the job done, using cloud services to do so when it makes sense

MapReduce Design Patterns Building Effective Algorithms and Analytics for Hadoop and Other Systems

Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.

Big Data Analytics with R and Hadoop: Set up an integrated infrastructure of R and Hadoop to turn your data analytics into Big Data analytics

If you're an R developer looking to harness the power of big data analytics with Hadoop, then this book tells you everything you need to integrate the two. You'll end up capable of building a data analytics engine with huge potential.

Programming Hive: Data Warehouse and Query Language for Hadoop

Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop’s data warehouse infrastructure. You’ll quickly learn how to use Hive’s SQL dialect—HiveQL—to summarize, query, and analyze large datasets stored in Hadoop’s distributed filesystem.

Hadoop in Action

Hadoop in Action teaches readers how to use Hadoop and write MapReduce programs. The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs.

Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset

Many corporations are finding that the size of their data sets are outgrowing the capability of their systems to store and process them. The data is becoming too big to manage and use with traditional tools. The solution: implementing a big data system.

Learning Hadoop 2

Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

 

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