Oracle Database Programming with Visual Basic.NET Discover a detailed treatment of the practical considerations and applications of Oracle database programming with Visual Basic 2019 Oracle Database Programming with Visual Basic.NET: Concepts, Designs, and Implementations delivers a comprehensive exploration of the foundations of Oracle database programming using Visual Basic.NET. Using Visual Basic.NET 2019, Visual Studio.NET 2019, and Oracle 18c XE, the book introduces the Oracle database development system, Oracle SQL Developer and Modeler, and teaches readers how to implement a sample database solution. The distinguished author also demonstrates the use of dotConnect for Oracle to show readers how to create an effective connection to an Oracle 18c XE database. The current versions of the .NET framework, ASP.NET, and ASP.NET 4.7 are also explored and used to offer readers the most up to date web database programming techniques available today. The book provides practical example projects and detailed, line-by-line descriptions throughout to assist readers in the development of their database programming skill. Students will also benefit from the inclusion of: A thorough introduction to databases, including definitions, examples, descriptions of keys and relationships, and some database components in popular databases, like Access, SQL, and Oracle An exploration of ADO.NET, including its architecture and components, like the DataReader class, DataSet component, DataTable component, and the command and parameter classes A discussion of Language Integrated Query (LINQ), including its architecture and components, its relationship to objects, DataSet, Oracle, and Entities An explanation of how to access data in ASP.NET and ASP.NET Web Services with multiple real project examples. Perfect for college and university students taking courses related to database programming and applications, Oracle Database Programming with Visual Basic.NET will also earn a place in the libraries of programmers and software engineers seeking a comprehensive reference for database coding in Visual Basic.NET.

Cyber-Physical Systems: A Comprehensive Guide explores the complete sys-tem perspective, underlying theories, modelling, and the applications of Cyber Physical Systems (CPS). It aims to cover all topics ranging from discussion of ru-diments of the system, efficient management, to recent research challenges and issues. Editors aim to present the book in a self-sufficient manner and to achieve this, the book has been edited to include all the aspects of CPS. The book fo-cuses on the concept map of CPS including latest technological interventions; issues, challenges, and the integration of CPS with IoT & Big Data Analytics. This aims to bring together unique contributions on cyber-physical systems research and education with applications in industrial, agriculture, and medical domains. The main aim of the book is to provide a roadmap to the latest advancements to provide optimal solutions in the field of CPS. Features * Coverage of rudiments of the subject * Discussion of recent advancements in the associated field * Considers an audience of diverse domains * Suitable for students (both UG and PG level) and researchers in the field of CPS This book aims to present the emergence of Cyber Physical Systems in response to revolutionary advancements in IoT. While discussing the associated challenges, it also endeavors to devise efficient models which are competent to address these challenges. This book aims to cater to researchers and academicians working in the related field of CPS.

This book aims to provide theoretical and empirical frameworks and highlights the challenges and solutions with using Big Data for Corporate Social Responsibility (CSR) and Sustainability in the field of digital transformation and tourism. Sustainability, Big Data, and Corporate Social Responsibility: Evidence from the Tourism Industry offers a theoretical and empirical framework in the field of digital transformation and applies it to the tourism sector. It discusses Big Data used with CSR and sustainability for the improvement of innovation and highlights the challenges and prospects. It presents a modern insight and approach for use by decision-makers as an application to solve various problems and explores how data collection can shed light on consumer behavior making it possible to account for existing situations and plan for the future. This book is intended to provide a modern insight for researcher, students, professionals, and decision-makers on the application of Big Data to improve CSR and sustainability in the tourism sector.

The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges.

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity. This book focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing. It also offers insights into manufacturing, logistics, and supply chain, where systems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure. It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities. Researchers, scholars, and students from different countries will appreciate this book for its real-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers. Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing Offers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformation Discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure Covers the effects that the 4th Industrial Revolution has on industrial infrastructures Looks at industry change patterns and innovations that are speeding up industrial transformation activities Om Prakash Jena is currently working as an associate professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Sabyasachi Pramanik is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. Ahmed A. Elngar is an associate professor in the Faculty of Computers & Artificial Intelligence, Beni-Suef University, Egypt. He is also an associate professor in the College of Computer Information Technology, chair of the Scientific Innovation Research Group (SIRG), and director of the Technological and Informatics Studies Center (TISC), American University in the Emirates, United Arab Emirates.

"In what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters-he vividly illustrates why it matters." -Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San Jose State University, USA If data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences. Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build.

This book discusses the technological aspects for the implementation of Society 5.0. The foundation and recent advances of emerging technologies such as artificial intelligence, data science, Internet of Things, and Big Data for the realization of Society 5.0 are covered. Practical solutions to existing problems, examples, and case studies are also offered. Society 5.0 and the Future of Emerging Computational Technologies: Practical Solutions, Examples, and Case Studies discusses technologies such as machine learning, artificial intelligence, and Internet of Things for the implementation of Society 5.0. It offers a firm foundation and understanding of the recent advancements in various domains such as data analytics, neural networks, computer vision, and robotics, along with practical solutions to existing problems in fields such as healthcare, manufacturing industries, security, and infrastructure management. Applications and implementations are highlighted along with the correlation between technologies. Examples and case studies are presented throughout the book to augment text. This book can be used by research scholars in the engineering domain who wish to gain knowledge and contribute towards a modern and secure future society. The book will also be useful as a reference at universities for postgraduate students who are interested in technological advancements.

This book helps SAP architects and SAP Basis administrators deploy and operate SAP S/4HANA systems on the most common public cloud platforms. Market-leading cloud offerings are covered, including Amazon Web Services, Microsoft Azure, and Google Cloud. You will gain an end-to-end understanding of the initial implementation of SAP S/4HANA systems on those platforms. You will learn how to move away from the big monolithic SAP ERP systems and arrive at an environment with a central SAP S/4HANA system as the digital core surrounded by cloud-native services. The book begins by introducing the core concepts of Hyperscaler cloud platforms that are relevant to SAP. You will learn about the architecture of SAP S/4HANA systems on public cloud platforms, with specific content provided for each of the major platforms. The book simplifies the deployment of SAP S/4HANA systems in public clouds by providing step-by-step instructions and helping you deal with the complexity of such a deployment. Content in the book is based on best practices, industry lessons learned, and architectural blueprints, helping you develop deep insights into the operations of SAP S/4HANA systems on public cloud platforms. Reading this book enables you to build and operate your own SAP S/4HANA system in the public cloud with a minimum of effort. What You Will Learn Choose the right Hyperscaler platform for your future SAP S/4HANA workloads Start deploying your first SAP S/4HANA system in the public cloud Avoid typical pitfalls during your implementation Apply and leverage cloud-native services for your SAP S/4HANA system Save costs by choosing the right architecture and build a robust architecture for your most critical SAP systems Meet your business' criteria for availability and performance by having the right sizing in place Identify further use cases when operating SAP S/4HANA in the public cloud Who This Book Is For SAP architects looking for an answer on how to move SAP S/4HANA systems from on-premises into the cloud; those planning to deploy to one of the three major platforms from Amazon Web Services, Microsoft Azure, and Google Cloud Platform; and SAP Basis administrators seeking a detailed and realistic description of how to get started on a migration to the cloud and how to drive that cloud implementation to completion

This comprehensive book unveils the working relationship of blockchain and the fog/edge computing. The contents of the book have been designed in such a way that the reader will not only understand blockchain and fog/edge computing but will also understand their co-existence and their collaborative power to solve a range of versatile problems. The first part of the book covers fundamental concepts and the applications of blockchain-enabled fog and edge computing. These include: Internet of Things, Tactile Internet, Smart City; and E-challan in the Internet of Vehicles. The second part of the book covers security and privacy related issues of blockchain-enabled fog and edge computing. These include, hardware primitive based Physical Unclonable Functions; Secure Management Systems; security of Edge and Cloud in the presence of blockchain; secure storage in fog using blockchain; and using differential privacy for edge-based Smart Grid over blockchain. This book is written for students, computer scientists, researchers and developers, who wish to work in the domain of blockchain and fog/edge computing. One of the unique features of this book is highlighting the issues, challenges, and future research directions associated with Blockchain-enabled fog and edge computing paradigm. We hope the readers will consider this book a valuable addition in the domain of Blockchain and fog/edge computing.

In this book, Edosa explores common challenges which limit the value that organisations can get from data. What makes his book unique is that he also tackles one of the unspoken barriers to data adoption-fear. Fear of the unknown, fear of the intangible, fear of the investment needed and, yes, fear of losing your job to a machine. With his talent for distilling clarity from complexity, Edosa tackles this and many other challenges. -Tim Carmichael, Chief Data Officer, Chalhoub Group This book offers fresh insight about how to solve the interactional frictions that hamper the flow of data, information and knowledge across organisations. Yet, rather than being stuck with endless polarising debates such as breaking down silos, it shifts focus back towards the ultimate "to what end." -Jacky Wright, Chief Digital Officer (CDO), Microsoft US If you care about AI transformation, empowering people or advancing organisational success in an increasingly digital world, then you should read this book. -Yomi Ibosiola, Chief Data and Analytics Officer, Union Bank A retail giant already struggling due to the Covid-19 pandemic was faced with a disastrous situation when-at the end of a critical investment in an artificial intelligence project that had been meant to save money-it suddenly discovered that its implementation was likely to leave it worse off. An entire critical service stream within an insurer's production system crashed. This critical failure resulted in the detentions of fully insured motorists for allegedly not carrying required insurance. Making Data Work details these two scenarios as well as others illustrating the consequences that arise when organizations do not know how to make data work properly. It is a journey to determine what to do to "make data work" for ourselves and for our organisations. It is a journey to discover how to bring it all together so organisations can enable digital transformation, empower people, and advance organisational success. It is the journey to a world where data and technology finally live up to the hype and deliver better human outcomes, where artificial intelligence can move us from reacting to situations to predicting future occurrences and enabling desirable possibilities.

"In what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters-he vividly illustrates why it matters." -Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San Jose State University, USA If data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences. Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build.

Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.

There are many books written for the accounting and finance community. However, there are very few books written to help the non-financial career professionals who still need to understand the conceptual fundamentals of accounting and finance. In 20 years of teaching this material to non-financial professionals, Dr. Bonner has perfected a teaching approach that works to help the non-financial professional engage with the material to use financial information in leveraging their career without becoming overloaded with information that is not helpful to them. Learning this material takes repetition, application, and building the thinking processes necessary for effectiveness. Many think the challenge with finance is the math, but as this book will demonstrate, it is a conceptual problem. If you understand the conceptual framework, you will understand the math. Dr. Julie Bonner is currently a tenured professor at Central Washington University in the information technology and administrative management department. Her career has spanned business and education for over 30 years. Initially, she received a Bachelor of Science degree in accounting, whereafter she earned an MBA and then a doctorate in organizational leadership.

"This book bridges the fields of health care and data to clarify how to use data to manage pandemics. Written while COVID-19 was raging, it identifies both effective practices and misfires, and is grounded in clear, research-based explanations of pandemics and data strategy....The author has written an essential book for students and professionals in both health care and data. While serving the needs of academics and experts, the book is accessible for the general reader." - Eileen Forrester, CEO of Forrester Leadership Group, Author of CMMI for Services, Guidelines for Superior Service "...Rupa Mahanti explores the connections between data and the human response to the spread of disease in her new book,... She recognizes the value of data and the kind of insight it can bring, while at the same time recognizing that using data to solve problems requires not just technology, but also leadership and courage. This is a book for people who want to better understand the role of data and people in solving human problems." -- Laura Sebastian-Coleman, Author of Meeting the Challenges of Data Quality Management In contrast to the 1918 Spanish flu pandemic which occurred in a non-digital age, the timing of the COVID-19 pandemic intersects with the digital age, characterized by the collection of large amounts of data and sophisticated technologies. Data and technology are being used to combat this digital age pandemic in ways that were not possible in the pre-digital age. Given the adverse impacts of pandemics in general and the COVID-19 pandemic in particular, it is imperative that people understand the meaning, origin of pandemics, related terms, trajectory of a new disease, butterfly effect of contagious diseases, factors governing the pandemic potential of a disease, strategies to combat a pandemic, role of data, data sharing, data strategy, data governance, analytics, and data visualization in managing pandemics, pandemic myths, critical success factors in managing pandemics, and lessons learned. How Data Can Manage Global Health Pandemics: Analyzing and Understanding COVID-19 discusses these elements with special reference to COVID-19. Dr. Rupa Mahanti is a business and data consultant and has expertise in different data management disciplines, business process improvement, regulatory reporting, quality management, and more. She is the author of Data Quality (ASQ Quality Press) and the series Data Governance: The Way Forward (Springer).

Provides overview of security challenges of IoT and mitigation techniques with a focus on authorization and access control mechanisms Discusses behavioural analysis of threats and attacks using UML base modelling Covers use of Oauth2.0 Protocol and UMA for connecting web applications Includes Role Based Access Control (RBAC), Discretionary Access Control (DAC), Mandatory Access Control (MAC), and Permission Based Access Control (PBAC) Explores how to provide access to third party web applications through resource server by use of secured and reliable Oauth2.0 framework

utilizes the Consolidated Serial Homicide Offender Database, one of the largest and most robust open access databases of multiple murders available illustrated with in-depth case studies of SHOs, such as Felix Vail, Michael Sumpter, the Seminole Heights Killer, and the Austin Bomber provides commentary from those who have used these patterning methods in practice, in addition to laying out how to put the current suite of data tools to use within organizations

Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor's manual are also provided as online supplements. This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas.

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems. ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things. The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.

Explains concepts of Internet of Everything problems, research challenge goals, and vision in smart cities Based on the most recent innovations, and covering the major challenges in smart cities, between IoT and Big Data Examines security issues and challenges related to data-intensive advances in IoT Addresses the total information science challenges in Internet of Everything enabled technologies Covers the exploring and creating IoT environment related self-adaptive systems

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.
Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.
Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data miningA valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions

The emerging field of Data Science has had a large impact on science and society. This book explores how one distinguishing feature of Data Science - its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded - suggests serious implications for learning and education. Drawing from theories of learning and identity development in the learning sciences, this volume investigates the impacts of these complex relationships on how learners think about, use, and share data, including their understandings of data in light of history, race, geography, and politics. More than just using 'real world examples' to motivate students to work with data, this book demonstrates how learners' relationships to data shape how they approach those data with agency, as part of their social and cultural lives. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, socially aware, and transformative Data Science Education. The chapters in this book were originally published as a special issue of the Journal of the Learning Sciences.

A database management system (DBMS) is a collection of programs that enable users to create and maintain a database; it also consists of a collection of interrelated data and a set of programs to access that data. Hence, a DBMS is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The primary goal of a DBMS is to provide an environment that is both convenient and efficient to use in retrieving and storing database information. It is an interface between the user of application programs, on the one hand, and the database, on the other. The objective of Database Management System: An Evolutionary Approach, is to enable the learner to grasp a basic understanding of a DBMS, its need, and its terminologies discern the difference between the traditional file-based systems and a DBMS code while learning to grasp theory in a practical way study provided examples and case studies for better comprehension This book is intended to give under- and postgraduate students a fundamental background in DBMSs. The book follows an evolutionary learning approach that emphasizes the basic concepts and builds a strong foundation to learn more advanced topics including normalizations, normal forms, PL/SQL, transactions, concurrency control, etc. This book also gives detailed knowledge with a focus on entity-relationship (ER) diagrams and their reductions into tables, with sufficient SQL codes for a more practical understanding.

With the rapidly advancing fields of Data Analytics and Computational Statistics, it's important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

Get introduced to Azure Arc-enabled Data Services and the powerful capabilities to deploy and manage local, on-premises, and hybrid cloud data resources using the same centralized management and tooling you get from the Azure cloud. This book shows how you can deploy and manage databases running on SQL Server and Postgres in your corporate data center or any cloud as if they were part of the Azure platform. This second edition has been updated to the latest codebase, allowing you to use this book as your handbook to get started with Azure Arc-enabled Data Services today. Learn how to benefit from Azure's centralized management, the automated rollout of patches and updates, managed backups, and more. This book is the perfect choice for anyone looking for a hybrid or multi-vendor cloud strategy for their data estate. The authors walk you through the possibilities and requirements to get Azure SQL Managed Instance and PostgresSQL Hyperscale deployed outside of Azure, so the services are accessible to companies that cannot move to the cloud or do not want to use the Microsoft cloud exclusively. The technology described in this book will benefit those required to keep sensitive services, such as medical databases, away from the public cloud equally as those who can't move to a public cloud for other reasons such as infrastructure constraints but still want to benefit from the Azure cloud and the centralized management and tooling that it supports. What You Will Learn Understand the fundamentals and architecture of Azure Arc-enabled data services Build a multi-cloud strategy based on Azure Data Services Deploy Azure Arc-enabled data services on premises or in any cloud Deploy Azure Arc-enabled SQL Managed Instance on premises or in any cloud Deploy Azure Arc-enabled PostgreSQL Hyperscale on premises or in any cloud Backup and Restore your data that is managed by Azure Arc-enabled data services Manage Azure-enabled data services running outside of Azure Monitor Azure-enabled data services through Grafana and Kibana Monitor Azure-enabled data services running outside of Azure through Azure Monitor Who This Book Is For Database administrators and architects who want to manage on-premises or hybrid cloud data resources from the Microsoft Azure cloud. Especially for those wishing to take advantage of cloud technologies while keeping sensitive data on premises and under physical control.

Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems. Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.