Today enterprises generate huge volumes of data. In order to provide effective services and to make smarter and more intelligent decisions from these huge volumes of data, enterprises use big-data analytics. In recent years, Hadoop has been used for massive data storage and efficient distributed processing of data. The Yet Another Resource Negotiator (YARN) framework solves the design problems related to resource management faced by the Hadoop 1.x framework
by providing a more scalable, efficient, flexible, and highly available resource management framework for distributed data processing.
This book starts with an overview of the YARN features and explains how YARN provides a business solution for growing big data needs. You will learn to provision and manage single, as well as multi-node, Hadoop-YARN clusters in the easiest way. You will walk through the YARN administration, life cycle management, application execution, REST APIs, schedulers, security framework and so on. You will gain insights about the YARN components and features such as ResourceManager, NodeManager, ApplicationMaster, Container, Timeline Server, High
Availability, Resource Localisation and so on.
The book explains Hadoop-YARN commands and the configurations of components and explores topics such as High Availability, Resource Localization and Log aggregation. You will then be ready to develop your own ApplicationMaster and execute it over a Hadoop-YARN cluster.
Towards the end of the book, you will learn about the security architecture and integration of YARN with big data technologies like Spark and Storm. This book promises conceptual as well as practical knowledge of resource management using YARN.
What you will learn
Explore YARN features and offerings
Manage big data clusters efficiently using the YARN framework
Create single as well as multinode HadoopYARN clusters on Linux machines
Understand YARN components and their administration
Gain insights into application execution flow over a YARN cluster
Write your own distributed application and execute it over YARN cluster
Work with schedulers and queues for efficient scheduling of applications
Integrate big data projects like Spark and Storm with YARN
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