Complete Guide to Open Source Big Data Stack

Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: The Big Data Stack Overview; What Is Big Data?; Limitations of Approach; Why a Stack?; NoSQL Overview; Development Stacks; LAMP Stack; MEAN Stack; SMACK Stack; MARQS Stack; Book Approach; Chapter 2 â#x80;#x93; Cloud Storage; Chapter 3 â#x80;#x93; Release Management â#x80;#x93; Brooklyn; Chapter 4 â#x80;#x93; Resource Management; Chapter 5 â#x80;#x93; Storage; Chapter 6 â#x80;#x93; Processing; Chapter 7 â#x80;#x93; Streaming; Chapter 8 â#x80;#x93; Frameworks; Chapter 9 â#x80;#x93; Data Visualisation; Chapter 10 â#x80;#x93; The Big Data Stack.;See a Mesos-based 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. In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack--sharing how to source the software and how to install it. You learn by simple example, step by step and chapter by chapter, as a real big data stack is created. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resource 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, Mesos, Kafka, and Zeppelin See how Brooklyn can be used to install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing Use Apache Spark for big data stack data processing. See a Mesos-based 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. In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack—sharing how to source the software and how to install it. You learn by simple example, step by step and chapter by chapter, as a real big data stack is created. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resource 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, Mesos, Kafka, and Zeppelin See how Brooklyn can be used to install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing 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 anyone interested in Hadoop or big data, and those experiencing problems with data size. "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, New Zealand author, 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."-- Provided by publisher Front Matter ....Pages i-xx The Big Data Stack Overview (Michael Frampton)....Pages 1-15 Cloud Storage (Michael Frampton)....Pages 17-58 Apache Brooklyn (Michael Frampton)....Pages 59-95 Apache Mesos (Michael Frampton)....Pages 97-137 Stack Storage Options (Michael Frampton)....Pages 139-175 Processing (Michael Frampton)....Pages 177-217 Streaming (Michael Frampton)....Pages 219-257 Frameworks (Michael Frampton)....Pages 259-294 Visualisation (Michael Frampton)....Pages 295-337 The Big Data Stack (Michael Frampton)....Pages 339-356 Back Matter ....Pages 357-365