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February 15 , 2010

Virtualizing Hadoop

How to Install, Deploy, and Optimize Hadoop in a Virtualized Architecture


Plan and Implement Hadoop Virtualization for Maximum Performance, Scalability, and Business Agility

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Enterprises running Hadoop must absorb rapid changes in big data ecosystems, frameworks, products, and workloads. Virtualized approaches can offer important advantages in speed, flexibility, and elasticity. Now, a world-class team of enterprise virtualization and big data experts guide you through the choices, considerations, and tradeoffs surrounding Hadoop virtualization. The authors help you decide whether to virtualize Hadoop, deploy Hadoop in the cloud, or integrate conventional and virtualized approaches in a blended solution.


First, Virtualizing Hadoop reviews big data and Hadoop from the standpoint of the virtualization specialist. The authors demystify MapReduce, YARN, and HDFS and guide you through each stage of Hadoop data management. Next, they turn the tables, introducing big data experts to modern virtualization concepts and best practices.


Finally, they bring Hadoop and virtualization together, guiding you through the decisions you’ll face in planning, deploying, provisioning, and managing virtualized Hadoop. From security to multitenancy to day-to-day management, you’ll find reliable answers for choosing your best Hadoop strategy and executing it.


Coverage includes the following:

•Reviewing the frameworks, products, distributions, use cases, and roles associated with Hadoop

•Understanding YARN resource management, HDFS storage, and I/O

•Designing data ingestion, movement, and organization for modern enterprise data platforms

•Defining SQL engine strategies to meet strict SLAs

•Considering security, data isolation, and scheduling for multitenant environments

•Deploying Hadoop as a service in the cloud

•Reviewing the essential concepts, capabilities, and terminology of virtualization

•Applying current best practices, guidelines, and key metrics for Hadoop virtualization

•Managing multiple Hadoop frameworks and products as one unified system

•Virtualizing master and worker nodes to maximize availability and performance

•Installing and configuring Linux for a Hadoop environment

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