||Hadoop MapReduce v2 Cookbook, 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.
Finally, you will be able to apply the knowledge you have gained to your own real-world scenarios to achieve the best-possible results. ...
||Apache Hadoop YARN|
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 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. ...
||Hadoop MapReduce Cookbook|
Learn to process large and complex data sets, starting simply, then diving in deep. Solve complex big data problems such as classifications, finding relationships, online marketing and recommendations. More than 50 Hadoop MapReduce recipes, presented in a simple and straightforward manner, with step-by-step instructions and real world examples. ...
||Programming Elastic MapReduce|
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 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. ...
||MapReduce Design Patterns|
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 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. ...
||Writing and Querying MapReduce Views in CouchDB|
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. ...
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