Anupama Raman - Böcker

This practical text/reference provides an exhaustive guide to setting up and sustaining software-defined data centers (SDDCs). Each of the core elements and underlying technologies are explained in detail, often supported by real-world examples. The text illustrates how cloud integration, brokerage, and orchestration can ensure optimal performance and usage of data resources, and what steps are required to secure each component in a SDDC. The coverage also includes material on hybrid cloud concepts, cloud-based data analytics, cloud configuration, enterprise DevOps and code deployment tools, and cloud software engineering.Topics and features: highlights how technologies relating to cloud computing, IoT, blockchain, and AI are revolutionizing business transactions, operations, and analytics; introduces the concept of Cloud 2.0, in which software-defined computing, storage, and networking are applied to produce next-generation cloud centers; examines software-defined storage for storage virtualization, covering issues of cloud storage, storage tiering, and deduplication; discusses software-defined networking for network virtualization, focusing on techniques for network optimization in data centers; reviews the qualities and benefits of hybrid clouds, that bridge private and public cloud environments; investigates the security management of a software-defined data center, and proposes a framework for managing hybrid IT infrastructure components; describes the management of multi-cloud environments through automated tools, and cloud brokers that aim to simplify cloud access, use and composition; covers cloud orchestration for automating application integration, testing, infrastructure provisioning, software deployment, configuration, and delivery.This comprehensive work is an essential reference for all practitioners involved with software-defined data center technologies, hybrid clouds, cloud service management, cloud-based analytics, and cloud-based software engineering.

This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.

This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data;