راهنمای کامل برای استک داده‌های بزرگ متن‌باز

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. 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. The Full StackCloud or Cluster The Future Chapter 2: Cloud Storage CloudStack Overview Server Preparation Minimum System Requirements Management Server Requirements Hypervisor Host Requirements Check CentOS Install Secure Shell (SSH) Access Configure Network Check Hostname FQDN Configure SELinux Configure NTP Configure CloudStack Package Repository Configure NFS (Network File System) CloudStack Server Install MySQL Server Install MySQL Connector Installation Management Server Installation System Template Setup KVM Setup and Installation Prerequisites. Create Repository FileKVM Installation KVM QEMU (Quick Emulator) Configuration Libvirt Configuration Check KVM Running Host Naming CloudStack Cluster Configuration Adding Hosts to the Cloud Adding an Instance to the Cloud Registering an ISO with CloudStack Creating an Instance from an ISO Advanced Zone Creation Problem-Solving CloudStack Log Files CloudStack Storage CloudStack System VMs CloudStack Firewall Issues Conclusion Chapter 3: Apache Brooklyn Brooklyn Install Brooklyn Overview Blueprints REST API Policy Management Monitoring Operations. Modelling With BlueprintsApplication Installs Server-Based Install Cloud-Based Install Conclusion Chapter 4: Apache Mesos Mesos Architecture Mesos Install Overview Building Mesos Mesos System Requirements Mesos Build Starting Mesos Mesos User Interface Build Errors Mesosphere DCOS Overview SSH configuration Install Prerequisites Install Server Master Server Agent Server User Interfaces Logging and Problem Investigation Build Errors Project Myriad Myriad Architecture Conclusion Chapter 5: Stack Storage Options HDFS Mesos Framework Source Software Start Scheduler. Create and Start HDFS NodesUse HDFS Mesos Framework Riak Mesos Framework VirtualBox Install Vagrant Install Install Framework Use Framework Cassandra Mesos Framework Install Prerequisites Install X Windows Install VirtualBox and Vagrant Install Vagrant-Based DCOS Install Cassandra Conclusion Chapter 6: Processing Stack Architecture Server Preparation Mesos and Spark Build Mesos Part 1 Build Mesos Part 2 Build Mesos Part 3 Building the Mesos Source Starting Mesos Installing the HDFS Framework Running Spark DCOS and Spark DCOS Build Part 1 DCOS Build Part 2. DCOS Build Part 3â#x80 #x94 Install Server. 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