Data Storage TechnologiesK.L.JAMESInformation has become very costly and indispensable in recent times. Information is present everywhere and is created continuously. It can be commonly seen that organizations are struggling hard for properly and effectively managing the huge volumes and varieties of information flowing through business systems, as the quantum of information is increasing with the passing of time. The use of information in its originally created form and style creates several difficulties and hence this is not done. The essentially required data is extracted from the huge information pile by employing suitable methods and appropriate technologies. The extracted data is properly stored for archiving and effective retrieving. Storing all the routine information dealt by organizations is costly and difficult. With the changes in requirements and technologies, businesses are constantly exploring and experimenting new methods for properly storing and effectively managing data. The ageing storage architectures and technologies are found too costly in terms of cost and time spent for effective data storage and management. Newer technologies such as converged and hyper converged infrastructures, public and private clouds, scaled up and scaled out devices, flash technology and software defined solutions are making the storage efficient and better. The text book Data Storage Technologies discusses the different concepts associated with data storage and the technologies used as well as the trends and practices followed in the designing and managing of efficient data storage systems. The book is targeted for students undergoing courses in data storage systems as well as for professionals working in related fields. The objective of the book is to introduce to the reader the basic concepts associated with data storage, steps necessary for designing well suited data storage solutions and the effective ways of using new technologies for data storage and management. Starting with an introductory chapter dealing with the basic concepts, the book progresses to discuss new topics like storage virtualization and cloud storage. The end chapter worksheets and short answer questions act as resources for self assessment and to test the new knowledge gained from each chapter of the book. Elegant and simple style of presentation makes reading of this book a pleasant experience. The book will be found highly acceptable to those who are interested to get acquainted with the latest and the newest trends associated with data storage technologies.

This book describes cloud computing as a service that is "highly scalable" and operates in "a resilient environment". The authors emphasize architectural layers and models - but also business and security factors.

Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.

Data Architecture: From Zen to Reality explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. Using a holistic approach to the field of data architecture, the book describes proven methods and technologies to solve the complex issues dealing with data. It covers the various applied areas of data, including data modelling and data model management, data quality, data governance, enterprise information management, database design, data warehousing, and warehouse design. This text is a core resource for anyone customizing or aligning data management systems, taking the Zen-like idea of data architecture to an attainable reality. The book presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios. It teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions. It includes the detail needed to illustrate how the fundamental principles are used in current business practice. The book is divided into five sections, one of which addresses the software-application development process, defining tools, techniques, and methods that ensure repeatable results. Data Architecture is intended for people in business management involved with corporate data issues and information technology decisions, ranging from data architects to IT consultants, IT auditors, and data administrators. It is also an ideal reference tool for those in a higher-level education process involved in data or information technology management. - Presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios - Teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions - Includes the detail needed to illustrate how the fundamental principles are used in current business practice

This important text provides a single point of reference for state-of-the-art cloud computing design and implementation techniques. The book examines cloud computing from the perspective of enterprise architecture, asking the question; how do we realize new business potential with our existing enterprises? Topics and features: with a Foreword by Thomas Erl; contains contributions from an international selection of preeminent experts; presents the state-of-the-art in enterprise architecture approaches with respect to cloud computing models, frameworks, technologies, and applications; discusses potential research directions, and technologies to facilitate the realization of emerging business models through enterprise architecture approaches; provides relevant theoretical frameworks, and the latest empirical research findings.

Distributed systems intertwine with our everyday lives. The benefits and current shortcomings of the underpinning technologies are experienced by a wide range of people and their smart devices. With the rise of large-scale IoT and similar distributed systems, cloud bursting technologies, and partial outsourcing solutions, private entities are encouraged to increase their efficiency and offer unparalleled availability and reliability to their users. The Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing is a vital reference source that provides valuable insight into current and emergent research occurring within the field of distributed computing. It also presents architectures and service frameworks to achieve highly integrated distributed systems and solutions to integration and efficient management challenges faced by current and future distributed systems. Highlighting a range of topics such as data sharing, wireless sensor networks, and scalability, this multi-volume book is ideally designed for system administrators, integrators, designers, developers, researchers, academicians, and students.