Angielski
See a big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together. This book describes the creation of an actual generic open source big data stack, which is an integrated stack of big data components, each of which serves a specific function such as storage, resource management, or queueing. Each component has a big data heritage and community to support it. Components can support big data in that they are able to scale, and are distributed and robust systems. In the Complete Guide to Open Source Big Data Stack, Mike Frampton begins by creating a private cloud and then by installing and examining Apache Brooklyn. After that he uses each chapter to introduce one piece of the big data stack-sharing how to source the software and then how to install it. He shows you how it works by simple example, step by step and chapter by chapter, and creates a real big data stack. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resources management, processing, queuing, frameworks, data visualization, and more. What You'll Learn Install a private cloud onto the local cluster using Apache cloud stack Source, install, and configure Apache: Brooklyn, Kafka, Mesos, and Zeppelin See how Brooklyn can be used to install Hadoop, Cassandra, and Riak, and how data can be moved Use Apache Spark for big data stack data processing Who This Book Is For Developers, architects, IT project managers, database administrators, and others charged with developing or supporting a big data system. It is also for a general IT audience, anyone interested in Hadoop or big data, and those experiencing problems with data size. It is also for anyone who would like to further their career in this area by adding big data skills.
