Mastering Mesos


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Mastering Mesos


Mastering Mesos

Author: Dipa Dubhashi

language: en

Publisher: Packt Publishing Ltd

Release Date: 2016-05-26


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The ultimate guide to managing, building, and deploying large-scale clusters with Apache Mesos About This Book Master the architecture of Mesos and intelligently distribute your task across clusters of machines Explore a wide range of tools and platforms that Mesos works with This real-world comprehensive and robust tutorial will help you become an expert Who This Book Is For The book aims to serve DevOps engineers and system administrators who are familiar with the basics of managing a Linux system and its tools What You Will Learn Understand the Mesos architecture Manually spin up a Mesos cluster on a distributed infrastructure Deploy a multi-node Mesos cluster using your favorite DevOps See the nuts and bolts of scheduling, service discovery, failure handling, security, monitoring, and debugging in an enterprise-grade, production cluster deployment Use Mesos to deploy big data frameworks, containerized applications, or even custom build your own applications effortlessly In Detail Apache Mesos is open source cluster management software that provides efficient resource isolations and resource sharing distributed applications or frameworks. This book will take you on a journey to enhance your knowledge from amateur to master level, showing you how to improve the efficiency, management, and development of Mesos clusters. The architecture is quite complex and this book will explore the difficulties and complexities of working with Mesos. We begin by introducing Mesos, explaining its architecture and functionality. Next, we provide a comprehensive overview of Mesos features and advanced topics such as high availability, fault tolerance, scaling, and efficiency. Furthermore, you will learn to set up multi-node Mesos clusters on private and public clouds. We will also introduce several Mesos-based scheduling and management frameworks or applications to enable the easy deployment, discovery, load balancing, and failure handling of long-running services. Next, you will find out how a Mesos cluster can be easily set up and monitored using the standard deployment and configuration management tools. This advanced guide will show you how to deploy important big data processing frameworks such as Hadoop, Spark, and Storm on Mesos and big data storage frameworks such as Cassandra, Elasticsearch, and Kafka. Style and approach This advanced guide provides a detailed step-by-step account of deploying a Mesos cluster. It will demystify the concepts behind Mesos.

Mastering Data Containerization and Orchestration


Mastering Data Containerization and Orchestration

Author: Cybellium

language: en

Publisher: Cybellium Ltd

Release Date:


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Your Guide to Streamlined Data Management In a data-driven world, the ability to manage and scale applications efficiently is key. "Mastering Data Containerization and Orchestration" is your roadmap to mastering the techniques that enable agile deployment, scaling, and management of applications. This book dives deep into containerization and orchestration, equipping you with the skills needed to excel in modern data management. Key Features: Container Fundamentals: Understand containers, Docker, and Kubernetes—the tools revolutionizing application packaging and execution. Efficient Scaling: Learn to optimize resource utilization and seamlessly scale applications, meeting user demands with ease. Application Lifecycle: Discover best practices for deploying, updating, and managing applications consistently. Microservices Mastery: Explore how containers enable the microservices pattern, enhancing application flexibility. Hybrid Environments: Navigate multi-cloud deployments while maintaining application consistency across platforms. Security Focus: Implement container security best practices to safeguard your applications and ensure compliance. Real-world Insights: Gain from real-world cases where containerization and orchestration drive business transformation. Why This Book Matters: In a rapidly evolving tech landscape, efficient application management is critical. "Mastering Data Containerization and Orchestration" empowers DevOps engineers, architects, and tech enthusiasts to excel in modern data management. Who Should Read: DevOps Engineers Software Architects System Administrators Tech Leaders Students and Learners Unlock Efficient Data Management: As data volumes surge, streamlined management is a must. "Mastering Data Containerization and Orchestration" equips you to navigate the complexities, transforming how you build, deploy, and manage applications. Your journey to successful modern data management starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Mastering Spark with R


Mastering Spark with R

Author: Javier Luraschi

language: en

Publisher: O'Reilly Media

Release Date: 2019-10-07


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If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions