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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. 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.
Fog computing is quickly increasing its applications and uses to
the next level. As it continues to grow, different types of
virtualization technologies can thrust this branch of computing
further into mainstream use. The Handbook of Research on Cloud and
Fog Computing Infrastructures for Data Science is a key reference
volume on the latest research on the role of next-generation
systems and devices that are capable of self-learning and how those
devices will impact society. Featuring wide-ranging coverage across
a variety of relevant views and themes such as cognitive analytics,
data mining algorithms, and the internet of things, this
publication is ideally designed for programmers, IT professionals,
students, researchers, and engineers looking for innovative
research on software-defined cloud infrastructures and
domain-specific analytics.
Learn the importance of architectural and design patterns in
producing and sustaining next-generation IT and business-critical
applications with this guide. About This Book * Use patterns to
tackle communication, integration, application structure, and more
* Implement modern design patterns such as microservices to build
resilient and highly available applications * Choose between the
MVP, MVC, and MVVM patterns depending on the application being
built Who This Book Is For This book will empower and enrich IT
architects (such as enterprise architects, software product
architects, and solution and system architects), technical
consultants, evangelists, and experts. What You Will Learn *
Understand how several architectural and design patterns work to
systematically develop multitier web, mobile, embedded, and cloud
applications * Learn object-oriented and component-based software
engineering principles and patterns * Explore the frameworks
corresponding to various architectural patterns * Implement
domain-driven, test-driven, and behavior-driven methodologies *
Deploy key platforms and tools effectively to enable EA design and
solutioning * Implement various patterns designed for the cloud
paradigm In Detail Enterprise Architecture (EA) is typically an
aggregate of the business, application, data, and infrastructure
architectures of any forward-looking enterprise. Due to constant
changes and rising complexities in the business and technology
landscapes, producing sophisticated architectures is on the rise.
Architectural patterns are gaining a lot of attention these days.
The book is divided in three modules. You'll learn about the
patterns associated with object-oriented, component-based,
client-server, and cloud architectures. The second module covers
Enterprise Application Integration (EAI) patterns and how they are
architected using various tools and patterns. You will come across
patterns for Service-Oriented Architecture (SOA), Event-Driven
Architecture (EDA), Resource-Oriented Architecture (ROA), big data
analytics architecture, and Microservices Architecture (MSA). The
final module talks about advanced topics such as Docker containers,
high performance, and reliable application architectures. The key
takeaways include understanding what architectures are, why they're
used, and how and where architecture, design, and integration
patterns are being leveraged to build better and bigger systems.
Style and Approach This book adopts a hands-on approach with
real-world examples and use cases.
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Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
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