Books: 29

MapReduce

CoverTitleYear
Harness the power of Couchbase to build flexible and scalable applications
Couchbase is an extremely fast, flexible, and highly scalable database that applies MapReduce techniques and patterns to find data. Whether you need to store unstructured data or be able to store and retrieve vast amounts of data quickly, Couchbase Server is your database! This book gives you enough more » information to successfully set up a Couchbase cluster and provides details on basic server maintenance. It then goes on to give you a detailed overview of how to program against Couchbase Server using both its key/value features and its document capabilities. It also introduces you to the concept of creating views using Couchbase's implementation of MapReduce. This book will then conclude with a walkthrough of building an actual application from scratch using Couchbase. « less
2015
Explore the Hadoop MapReduce v2 ecosystem to gain insights from very large datasets
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. more » 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. Finally, you will be able to apply the knowledge you have gained to your own real-world scenarios to achieve the best-possible results. « less
2015
Implement fast, lean, and readable code effectively with Lo-Dash
Lo-Dash Essentials walks you through the Lo-Dash utility library, which promises consistency and performance in JavaScript development. This book looks into the most common functions and the various contexts in which they're used. You'll first start with object types and their properties, then you'll more » dive into larger development patterns, such as MapReduce, and how to chain functionality together. Following this, you'll learn how to make suitable builds for various environments, and discover how high-level patterns complement one another and how they lead to reusable building blocks for applications. Finally, you will gain some practical exposure to Lo-Dash by working alongside other libraries, and learn some useful techniques for improving performance. « less
2015
Moving beyond MapReduce and Batch Processing with Apache Hadoop 2
Apache Hadoop is helping drive the Big Data revolution. Now, its data processing has been completely overhauled: Apache Hadoop YARN provides resource management at data center scale and easier ways to create distributed applications that process petabytes of data. And now in Apache Hadoop YARN, two Hadoop more » technical leaders show you how to develop new applications and adapt existing code to fully leverage these revolutionary advances. YARN project founder Arun Murthy and project lead Vinod Kumar Vavilapalli demonstrate how YARN increases scalability and cluster utilization, enables new programming models and services, and opens new options beyond Java and batch processing. They walk you through the entire YARN project lifecycle, from installation through deployment. « less
2014
Pro Apache Hadoop, Second Edition brings you up to speed on Hadoop - the framework of big data. Revised to cover Hadoop 2.0, the book covers the very latest developments such as YARN (aka MapReduce 2.0), new HDFS high-availability features, and increased scalability in the form of HDFS Federations. All more » the old content has been revised too, giving the latest on the ins and outs of MapReduce, cluster design, the Hadoop Distributed File System, and more. This book covers everything you need to build your first Hadoop cluster and begin analyzing and deriving value from your business and scientific data. Learn to solve big-data problems the MapReduce way, by breaking a big problem into chunks and creating small-scale solutions that can be flung across thousands upon thousands of nodes to analyze large data volumes in a short amount of wall-clock time. Learn how to let Hadoop take care of distributing and parallelizing your software - you just focus on the code; Hadoop takes care of the rest. « less
2014
Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. more » It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth. With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them. « less
2013
Using AWS Services to Build an End-to-End Application
Although you don't need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce (EMR), the hosted Hadoop more » framework in Amazon Web Services (AWS). Authors Kevin Schmidt and Christopher Phillips demonstrate best practices for using EMR and various AWS and Apache technologies by walking you through the construction of a sample MapReduce log analysis application. Using code samples and example configurations, you'll learn how to assemble the building blocks necessary to solve your biggest data analysis problems. « less
2013
Building Effective Algorithms and Analytics for Hadoop and Other Systems
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework more » you're using. 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. « less
2012
Step-by-step instructions and practical examples to creating web applications with Ruby and MongoDB. Learn to design the object model in a NoSQL way. Create objects in Ruby and map them to MongoDB. Learn about Mongoid and MongoMapper for mapping Ruby objects to MongoDB documents. Process large datasets more » with MapReduce. Create geo-spatial indexes or 2D indexes. « less
2012
Tools for Data Analysts
Learn how to create MapReduce views in CouchDB that let you query the document-oriented database for meaningful data. With this short and concise ebook, you'll get step-by-step instructions and lots of sample code to create and explore several MapReduce views, using an example database you construct more ». « less
2011