Complete Guide to Open Source Big Data Stack

2017 | Computing & IT

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 like storage, resource management, or queueing. Each component has a big data heritage and community to support it. It can support big data in that it is able to scale, and it is a distributed and robust system. 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 will use each chapter to introduce one piece of the big data stack-sharing how to source the software and then how to install it. He will then show how it works by simple example. Step by step and chapter by chapter, Frampton will create a real big data stack. The goal of this book is to show how a big data stack might be created and what components might be used. It attempts to do this with currently available Apache full and incubating systems. The aim is to introduce these components by example and show how they might work together.

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 * How to install a private cloud onto the local cluster using Apache cloud stack. * How to source and install Apache Brooklyn. * How to source and install Mesos. * How Brooklyn can be used to install Hadoop, Cassandra, and Riak and how data can be moved. * How to use Apache Spark for big data stack data processing. * How Apache Kafka can be sourced, installed and configured. * How to source and install Apache Zeppelin the big data visualization system. Who This Book Is For 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's also for anyone who would like to further their career in this area by adding big data skills.

Mike Frampton (Author)