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Cloud Computing
Cloud computing has created a shift from the use of physical hardware and locally managed software-enabled platforms to that of virtualized cloud-hosted services. Cloud assembles large networks of virtual services, including hardware (CPU, storage, and network) and software resources (databases, message queuing systems, monitoring systems, and load-balancers). As Cloud continues to revolutionize applications in academia, industry, government, and many other fields, the transition to this efficient and flexible platform presents serious challenges at both theoretical and practical levels—ones that will often require new approaches and practices in all areas. Comprehensive and timely, Cloud ...
Cloud Data Management
In practice, the design and architecture of a cloud varies among cloud providers. We present a generic evaluation framework for the performance, availability and reliability characteristics of various cloud platforms. We describe a generic benchmark architecture for cloud databases, specifically NoSQL database as a service. It measures the performance of replication delay and monetary cost. Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. The specifications of existing service level agreements (SLA) for cloud services are not designed to flexibly handle even relatively straightforward performance and...
Reinventing the Social Scientist and Humanist in the Era of Big Data
This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences.
Large Scale and Big Data
Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing t
Linked Data Management
Linked Data Management presents techniques for querying and managing Linked Data that is available on today’s Web. The book shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications. The text focuses on aspects of managing large-scale collections of Linked Data. It offers a detailed introduction to Linked Data and related standards, including the main principles distinguishing Linked Data from standard database technology. Chapters also describe how to generate links between datasets and explain the overall architecture of data integration systems based on Linked Data. A large part of the text is devoted to query processing in different set...
Web Scalability for Startup Engineers
This invaluable roadmap for startup engineers reveals how to successfully handle web application scalability challenges to meet increasing product and traffic demands. Web Scalability for Startup Engineers shows engineers working at startups and small companies how to plan and implement a comprehensive scalability strategy. It presents broad and holistic view of infrastructure and architecture of a scalable web application. Successful startups often face the challenge of scalability, and the core concepts driving a scalable architecture are language and platform agnostic. The book covers scalability of HTTP-based systems (websites, REST APIs, SaaS, and mobile application backends), starting ...
Cassandra-Datenbank. Von der Theorie zur Praxis
Studienarbeit aus dem Jahr 2024 im Fachbereich Informatik - Künstliche Intelligenz, Note: 1.0, Fachhochschule Südwestfalen; Abteilung Iserlohn, Sprache: Deutsch, Abstract: Diese Hausarbeit konzentriert sich auf die Cassandra-Datenbanktechnologie und ihre Anwendung in der datenbasierten Entscheidungsfindung. Sie betont die Bedeutung von Datenbanken für die Verwaltung und Speicherung großer Datenmengen und hebt die Effizienz von Cassandra in Umgebungen mit hohen Skalierbarkeits- und Verteilungsanforderungen hervor. Die Arbeit untersucht sowohl die Theorie als auch die Praxis. Dabei zeigt eine Fallstudie zum Abrufen und Speichern von NASDAQ-Werten die Praxistauglichkeit von Cassandra auf. Herausforderungen und Stärken von Cassandra, einschließlich ihrer Skalierbarkeit, Fehlertoleranz und verteilten Architektur, werden detailliert erörtert. Abschließend wird die wachsende Bedeutung von Cassandra in der Echtzeit-Datenanalyse und Big-Data-Verarbeitung erläutert.
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