| cloud data management capabilities: Cloud Data Architectures Demystified Ashok Boddeda, 2023-09-27 Learn using Cloud data technologies for improving data analytics and decision-making capabilities for your organization KEY FEATURES ● Get familiar with the fundamentals of data architecture and Cloud computing. ● Design and deploy enterprise data architectures on the Cloud. ● Learn how to leverage AI/ML to gain insights from data. DESCRIPTION Cloud data architectures are a valuable tool for organizations that want to use data to make better decisions. By understanding the different components of Cloud data architectures and the benefits they offer, organizations can select the right architecture for their needs. This book is a holistic guide for using Cloud data technologies to ingest, transform, and analyze data. It covers the entire data lifecycle, from collecting data to transforming it into actionable insights. The readers will get a comprehensive overview of Cloud data technologies and AI/ML algorithms. The readers will learn how to use these technologies and algorithms to improve decision-making, optimize operations, and identify new opportunities. By the end of the book, you will have a comprehensive understanding of loud data architectures and the confidence to implement effective solutions that drive business success. WHAT YOU WILL LEARN ● Learn the fundamental principles of data architecture. ● Understand the working of different cloud ecosystems such as AWS, Azure & GCP. ● Explore different Snowflake data services. ● Learn how to implement data governance policies and procedures. ● Use artificial intelligence (AI) and machine learning (ML) to gain insights from data. WHO THIS BOOK IS FOR This book is for executives, IT professionals, and data enthusiasts who want to learn more about Cloud data architectures. It does not require any prior experience, but a basic understanding of data concepts and technology landscapes will be helpful. TABLE OF CONTENTS 1. Data Architectures and Patterns 2. Enterprise Data Architectures 3. Cloud Fundamentals 4. Azure Data Eco-system 5. AWS Data Services 6. Google Data Services 7. Snowflake Data Eco-system 8. Data Governance 9. Data Intelligence: AI-ML Modeling and Services |
| cloud data management capabilities: Grid and Cloud Database Management Sandro Fiore, Giovanni Aloisio, 2011-07-28 Since the 1990s Grid Computing has emerged as a paradigm for accessing and managing distributed, heterogeneous and geographically spread resources, promising that we will be able to access computer power as easily as we can access the electric power grid. Later on, Cloud Computing brought the promise of providing easy and inexpensive access to remote hardware and storage resources. Exploiting pay-per-use models and virtualization for resource provisioning, cloud computing has been rapidly accepted and used by researchers, scientists and industries. In this volume, contributions from internationally recognized experts describe the latest findings on challenging topics related to grid and cloud database management. By exploring current and future developments, they provide a thorough understanding of the principles and techniques involved in these fields. The presented topics are well balanced and complementary, and they range from well-known research projects and real case studies to standards and specifications, and non-functional aspects such as security, performance and scalability. Following an initial introduction by the editors, the contributions are organized into four sections: Open Standards and Specifications, Research Efforts in Grid Database Management, Cloud Data Management, and Scientific Case Studies. With this presentation, the book serves mostly researchers and graduate students, both as an introduction to and as a technical reference for grid and cloud database management. The detailed descriptions of research prototypes dealing with spatiotemporal or genomic data will also be useful for application engineers in these fields. |
| cloud data management capabilities: Data Management in Cloud, Grid and P2P Systems Abdelkader Hameurlain, Wenny Rahayu, David Taniar, 2013-08-21 This book constitutes the refereed proceedings of the 6th International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2013, held in Prague, Czech Republic, in August 2013 in conjunction with DEXA 2013. The 10 revised full papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: data partitioning and consistency; RDF data publishing, querying linked data, and applications; and distributed storage systems and virtualization. |
| cloud data management capabilities: Cloud Database: Empowering Scalable and Flexible Data Management Dr. A. Karunamurthy, M. Yuvaraj, J. Shahithya, V. Thenmozhi, 2023-03-30 This paper explores the concept of cloud database, which leverages the power of cloud computing to provide scalable and flexible data management solutions. It discusses the benefits, challenges, and considerations associated with adopting cloud databases, along with various architectural models and deployment options. The chapter also delves into the key features, such as elasticity, high availability, and data security, offered by cloud databases. Furthermore, it examines the role of cloud databases in modern applications, including their integration with other cloud services and their ability to support big data analytics. The chapter concludes by highlighting future trends and advancements in cloud database technologies. |
| cloud data management capabilities: Cloud Data Management Liang Zhao, Sherif Sakr, Anna Liu, Athman Bouguettaya, 2014-07-08 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 technical requirements of consumer applications. We present a novel approach for SLA-based management of cloud-hosted databases from the consumer perspective and an end-to-end framework for consumer-centric SLA management of cloud-hosted databases. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted software applications. |
| cloud data management capabilities: Ultimate Snowflake Architecture for Cloud Data Warehousing Ganesh Bharathan , 2024-04-25 Unlocking the Power of Snowflake: Unveiling the Architectural Wonders of Modern Data Management KEY FEATURES ● Learn from real client experiences for practical deployment and administration. ● Design secure and high-performance data architectures. ● Develop seamless data pipelines for creation, transformation, and consumption. ● Utilize Snowflake Data Exchange for collaborative data sets and insights. DESCRIPTION Unlock the revolutionary world of Snowflake with this comprehensive book which offers invaluable insights into every aspect of Snowflake architecture and management. Beginning with an introduction to Snowflake's architecture and key concepts, you will learn about cloud data warehousing principles like Star and Snowflake schemas to master efficient data organization. Advancing to topics such as distributed systems and data loading techniques, you will discover how Snowflake manages data storage and processing for scalability and optimized performance. Covering security features like encryption and access control, the book will equip you with the tools to ensure data confidentiality and compliance. The book also covers expert insights into performance optimization and schema design, equipping you with techniques to unleash Snowflake's full potential. By the end, you will have a comprehensive understanding of Snowflake's architecture and be empowered to leverage its features for valuable insights from massive datasets. WHAT WILL YOU LEARN ● Understand the foundational principles of Snowflake architecture and its core components ● Efficiently manage organizations and accounts within the Snowflake environment ● Leverage virtual warehouse compute to scale processing capabilities effectively ● Implement role-based access control to ensure robust data security measures ● Establish comprehensive data governance practices tailored to Snowflake ● Apply the security framework provided by Snowflake to safeguard data assets ● Implement deployment considerations for seamless integration into existing systems ● Optimize data storage strategies to maximize efficiency and performance ● Explore the Snowflake Marketplace for additional resources and solutions ● Extend Snowflake's functionality using Snowpark for enhanced data processing capabilities WHO IS THIS BOOK FOR? The book is designed for data professionals, including database administrators, data engineers, solution architects, and enterprise data architects, seeking to optimize their data management and analysis with Snowflake architecture. Proficiency in SQL, data warehousing, cloud computing, distributed systems, data loading/integration, security, performance optimization, and schema design are essential prerequisites. Whether you're a beginner, intermediate, or advanced user, this book caters to all proficiency levels. TABLE OF CONTENTS 1. Getting Started with Snowflake Architecture 2. Managing Organizations and Accounts 3. Virtual Warehouse Compute 4. Role-Based Access Control 5. Snowflake Data Governance 6. Snowflake Security Framework 7. Deployment Considerations 8. Data Storage in Snowflake 9. Snowflake Marketplace: 10. Snowpark Index |
| cloud data management capabilities: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives. |
| cloud data management capabilities: Introduction to Data Platforms Anthony David Giordano, 2022-11-03 Digital, cloud, and artificial intelligence (AI) have disrupted how we use data. This disruption has changed the way we need to provision, curate, and publish data for the multiple use cases in today's technology-driven environment. This text will cover how to design, develop, and evolve a data platform for all the uses of enterprise data needed in today's digital organization. This book focuses on explaining what a data platform is, what value it provides, how is it engineered, and how to deploy a data platform and support organization. In this context, Introduction to Data Platforms reviews the current requirements for data in the digital age and quantifies the use cases; discusses the evolution of data over the past twenty years, which is a core driver of the modern data platform; defines what a data platform is and defines the architectural components and layers of a data platform; provides the architectural layers or capabilities of a data platform; reviews cloud- and commercial-software vendors that populate the data-platform space; provides a step-by-step approach to engineering, deploying, supporting, and evolving a data-platform environment; provides a step-by-step approach to migrating legacy data warehouses, data marts, and data lakes/sandboxes to a data platform; and reviews organizational structures for managing data platform environments. |
| cloud data management capabilities: Building Cloud Data Platforms Solutions Anouar BEN ZAHRA, Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects. |
| cloud data management capabilities: Mastering Cloud Data Cybellium Ltd, 2023-09-06 Cybellium Ltd is dedicated to empowering individuals and organizations with the knowledge and skills they need to navigate the ever-evolving computer science landscape securely and learn only the latest information available on any subject in the category of computer science including: - Information Technology (IT) - Cyber Security - Information Security - Big Data - Artificial Intelligence (AI) - Engineering - Robotics - Standards and compliance Our mission is to be at the forefront of computer science education, offering a wide and comprehensive range of resources, including books, courses, classes and training programs, tailored to meet the diverse needs of any subject in computer science. Visit https://www.cybellium.com for more books. |
| cloud data management capabilities: The Cloud Data Lake Rukmani Gopalan, 2022-12-12 More organizations than ever understand the importance of data lake architectures for deriving value from their data. Building a robust, scalable, and performant data lake remains a complex proposition, however, with a buffet of tools and options that need to work together to provide a seamless end-to-end pipeline from data to insights. This book provides a concise yet comprehensive overview on the setup, management, and governance of a cloud data lake. Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance. Learn the benefits of a cloud-based big data strategy for your organization Get guidance and best practices for designing performant and scalable data lakes Examine architecture and design choices, and data governance principles and strategies Build a data strategy that scales as your organizational and business needs increase Implement a scalable data lake in the cloud Use cloud-based advanced analytics to gain more value from your data |
| cloud data management capabilities: Data Mangement in Cloud, Grid and P2P Systems Abdelkader Hameurlain, Farookh Khadeer Hussain, Franck Morvan, A Min Tjoa, 2012-08-19 This book constitutes the refereed proceedings of the 5th International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2012, held in Vienna, Austria, in September 2012 in conjunction with DEXA 2012. The 9 revised full papers presented were carefully reviewed and selected from 15 submissions. The papers are organized in topical sections on data management in the cloud, cloud MapReduce and performance evaluation, and data stream systems and distributed data mining. |
| cloud data management capabilities: Cloud Enterprise Architecture Pethuru Raj, 2012-10-24 Cloud Enterprise Architecture examines enterprise architecture (EA) in the context of the surging popularity of Cloud computing. It explains the different kinds of desired transformations the architectural blocks of EA undergo in light of this strategically significant convergence. Chapters cover each of the contributing architectures of EA—business, information, application, integration, security, and technology—illustrating the current and impending implications of the Cloud on each. Discussing the implications of the Cloud paradigm on EA, the book details the perceptible and positive changes that will affect EA design, governance, strategy, management, and sustenance. The author ties these topics together with chapters on Cloud integration and composition architecture. He also examines the Enterprise Cloud, Federated Clouds, and the vision to establish the InterCloud. Laying out a comprehensive strategy for planning and executing Cloud-inspired transformations, the book: Explains how the Cloud changes and affects enterprise architecture design, governance, strategy, management, and sustenance Presents helpful information on next-generation Cloud computing Describes additional architectural types such as enterprise-scale integration, security, management, and governance architectures This book is an ideal resource for enterprise architects, Cloud evangelists and enthusiasts, and Cloud application and service architects. Cloud center administrators, Cloud business executives, managers, and analysts will also find the book helpful and inspirational while formulating appropriate mechanisms and schemes for sound modernization and migration of traditional applications to Cloud infrastructures and platforms. |
| cloud data management capabilities: Fast and Scalable Cloud Data Management Felix Gessert, Wolfram Wingerath, Norbert Ritter, 2020-05-15 The unprecedented scale at which data is both produced and consumed today has generated a large demand for scalable data management solutions facilitating fast access from all over the world. As one consequence, a plethora of non-relational, distributed NoSQL database systems have risen in recent years and today’s data management system landscape has thus become somewhat hard to overlook. As another consequence, complex polyglot designs and elaborate schemes for data distribution and delivery have become the norm for building applications that connect users and organizations across the globe – but choosing the right combination of systems for a given use case has become increasingly difficult as well. To help practitioners stay on top of that challenge, this book presents a comprehensive overview and classification of the current system landscape in cloud data management as well as a survey of the state-of-the-art approaches for efficient data distribution and delivery to end-user devices. The topics covered thus range from NoSQL storage systems and polyglot architectures (backend) over distributed transactions and Web caching (network) to data access and rendering performance in the client (end-user). By distinguishing popular data management systems by data model, consistency guarantees, and other dimensions of interest, this book provides an abstract framework for reasoning about the overall design space and the individual positions claimed by each of the systems therein. Building on this classification, this book further presents an application-driven decision guidance tool that breaks the process of choosing a set of viable system candidates for a given application scenario down into a straightforward decision tree. |
| cloud data management capabilities: Google Cloud Database Engineer Certification , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| cloud data management capabilities: Cloud Computing Dinesh G. Harkut, Kashmira Kasat, Saurabh Shah, 2019-01-03 In the era of the Internet of Things and Big Data, Cloud Computing has recently emerged as one of the latest buzzwords in the computing industry. It is the latest evolution of computing, where IT recourses are offered as services. Cloud computing provides on-demand, scalable, device-independent, and reliable services to its users. The exponential growth of digital data bundled with the needs of analysis, processing and storage, and cloud computing has paved the way for a cheap, secure, and omnipresent computing framework allowing for the delivery of enormous computing and storage capacity to a diverse community of end-recipients. Clouds are distributed technology platforms that leverage sophisticated technology innovations to provide highly scalable and resilient environments that can be remotely utilized by organizations in a multitude of powerful ways. The term cloud is often used as a metaphor for the Internet and can be defined as a new type of utility computing that basically uses servers that have been made available to third parties via the Internet. |
| cloud data management capabilities: Machine Learning Security with Azure Georgia Kalyva, 2023-12-28 Implement industry best practices to identify vulnerabilities and protect your data, models, environment, and applications while learning how to recover from a security breach Key Features Learn about machine learning attacks and assess your workloads for vulnerabilities Gain insights into securing data, infrastructure, and workloads effectively Discover how to set and maintain a better security posture with the Azure Machine Learning platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWith AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure. This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture. By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.What you will learn Explore the Azure Machine Learning project life cycle and services Assess the vulnerability of your ML assets using the Zero Trust model Explore essential controls to ensure data governance and compliance in Azure Understand different methods to secure your data, models, and infrastructure against attacks Find out how to detect and remediate past or ongoing attacks Explore methods to recover from a security breach Monitor and maintain your security posture with the right tools and best practices Who this book is for This book is for anyone looking to learn how to assess, secure, and monitor every aspect of AI or machine learning projects running on the Microsoft Azure platform using the latest security and compliance, industry best practices, and standards. This is a must-have resource for machine learning developers and data scientists working on ML projects. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from this book, as the chapters cover everything from implementation to deployment, AI attack prevention, and recovery. |
| cloud data management capabilities: Automating Data Quality Monitoring Jeremy Stanley, Paige Schwartz, 2024-01-09 The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term |
| cloud data management capabilities: Developing and Securing the Cloud Bhavani Thuraisingham, 2013-10-28 Although the use of cloud computing platforms and applications has expanded rapidly, most books on the subject focus on high-level concepts. There has long been a need for a book that provides detailed guidance on how to develop secure clouds.Filling this void, Developing and Securing the Cloud provides a comprehensive overview of cloud computing t |
| cloud data management capabilities: Building Blocks for IoT Analytics Internet-of-Things Analytics John Soldatos, 2022-09-01 Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI). |
| cloud data management capabilities: 2020 International Conference on Applications and Techniques in Cyber Intelligence Jemal H. Abawajy, Kim-Kwang Raymond Choo, Zheng Xu, Mohammed Atiquzzaman, 2020-08-12 This book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users. |
| cloud data management capabilities: The Future Internet Alex Galis, Anastasius Gavras, 2013-04-22 Co-editors of the volume are: Federico Álvarez, Alessandro Bassi, Michele Bezzi, Laurent Ciavaglia, Frances Cleary, Petros Daras, Hermann De Meer, Panagiotis Demestichas, John Domingue, Theo G. Kanter, Stamatis Karnouskos, Srdjan Krčo, Laurent Lefevre, Jasper Lentjes, Man-Sze Li, Paul Malone, Antonio Manzalini, Volkmar Lotz, Henning Müller, Karsten Oberle, Noel E. O'Connor, Nick Papanikolaou, Dana Petcu, Rahim Rahmani, Danny Raz, Gaël Richards, Elio Salvadori, Susana Sargento, Hans Schaffers, Joan Serrat, Burkhard Stiller, Antonio F. Skarmeta, Kurt Tutschku, Theodore Zahariadis The Internet is the most vital scientific, technical, economic and societal set of infrastructures in existence and in operation today serving 2.5 billion users. Continuing its developments would secure much of the upcoming innovation and prosperity and it would underpin the sustainable growth in economic values and volumes needed in the future. Future Internet infrastructures research is therefore a must. The Future Internet Assembly (FIA) is a successful conference that brings together participants of over 150 research projects from several distinct yet interrelated areas in the European Union Framework Programme 7 (FP7). The research projects are grouped as follows: the network of the future as infrastructure connecting and orchestrating the future Internet of people, computers, devices, content, clouds and things; cloud computing, Internet of Services and advanced software engineering; the public-private partnership projects on Future Internet; Future Internet Research and Experimentation (FIRE). The 26 full papers included in this volume were selected from 45 submissions. They are organized in topical sections named: software driven networks, virtualization, programmability and autonomic management; computing and networking clouds; internet of things; and enabling technologies and economic incentives. |
| cloud data management capabilities: Handbook of Research on Cloud Infrastructures for Big Data Analytics Raj, Pethuru, 2014-03-31 Clouds are being positioned as the next-generation consolidated, centralized, yet federated IT infrastructure for hosting all kinds of IT platforms and for deploying, maintaining, and managing a wider variety of personal, as well as professional applications and services. Handbook of Research on Cloud Infrastructures for Big Data Analytics focuses exclusively on the topic of cloud-sponsored big data analytics for creating flexible and futuristic organizations. This book helps researchers and practitioners, as well as business entrepreneurs, to make informed decisions and consider appropriate action to simplify and streamline the arduous journey towards smarter enterprises. |
| cloud data management capabilities: T Bytes Platforms & Applications ITShades.com, 2020-10-28 This document brings together a set of latest data points and publicly available information relevant for Platforms & Applications Industry. We are very excited to share this content and believe that readers will benefit from this periodic publication immensely. |
| cloud data management capabilities: Service Oriented Infrastructures and Cloud Service Platforms for the Enterprise Theo Dimitrakos, Josep Martrat, Stefan Wesner, 2009-10-21 Service-Oriented Infrastructures including Grid and Cloud Computing are technologies in a critical transition to wider adoption by business. Their use may enable enterprises to achieve optimal IT utilization, including sharing resources and services across enterprises and on-demand utilization of those made available by business partners over the network. This book is an essential reference for researchers and practitioners in service-oriented IT. It analyses a selection of common capabilities (services capturing reusable functionality of IT solutions) that have been applied to tackle challenging business problems and were validated by the BEinGRID consortium in real-life business trials covering most European market sectors. |
| cloud data management capabilities: Grid and Cloud Computing Katarina Stanoevska, Thomas Wozniak, Santi Ristol, 2009-11-04 In today’s dynamic business environment, IT departments are under permanent pressure to meet two divergent requirements: to reduce costs and to support business agility with higher flexibility and responsiveness of the IT infrastructure. Grid and Cloud Computing enable a new approach towards IT. They enable increased scalability and more efficient use of IT based on virtualization of heterogeneous and distributed IT resources. This book provides a thorough understanding of the fundamentals of Grids and Clouds and of how companies can benefit from them. A wide array of topics is covered, e.g. business models and legal aspects. The applicability of Grids and Clouds in companies is illustrated with four cases of real business experiments. The experiments illustrate the technical solutions and the organizational and IT governance challenges that arise with the introduction of Grids and Clouds. Practical guidelines on how to successfully introduce Grids and Clouds in companies are provided. |
| cloud data management capabilities: Building the Hyperconnected Society- Internet of Things Research and Innovation Value Chains, Ecosystems and Markets Ovidiu Vermesan, Peter Friess, 2022-09-01 This book aims to provide a broad overview of various topics of Internet of Things (IoT), ranging from research, innovation and development priorities to enabling technologies, nanoelectronics, cyber-physical systems, architecture, interoperability and industrial applications. All this is happening in a global context, building towards intelligent, interconnected decision making as an essential driver for new growth and co-competition across a wider set of markets. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. The concept of IoT could disrupt consumer and industrial product markets generating new revenues and serving as a growth driver for semiconductor, networking equipment, and service provider end-markets globally. This will create new application and product end-markets, change the value chain of companies that creates the IoT technology and deploy it in various end sectors, while impacting the business models of semiconductor, software, device, communication and service provider stakeholders. The proliferation of intelligent devices at the edge of the network with the introduction of embedded software and app-driven hardware into manufactured devices, and the ability, through embedded software/hardware developments, to monetize those device functions and features by offering novel solutions, could generate completely new types of revenue streams. Intelligent and IoT devices leverage software, software licensing, entitlement management, and Internet connectivity in ways that address many of the societal challenges that we will face in the next decade. |
| cloud data management capabilities: Data Stewardship in Action Pui Shing Lee, 2024-02-16 Take your organization's data maturity to the next level by operationalizing data governance Key Features Develop the mindset and skills essential for successful data stewardship Apply practical advice and industry best practices, spanning data governance, quality management, and compliance, to enhance data stewardship Follow a step-by-step program to develop a data operating model and implement data stewardship effectively Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the competitive data-centric world, mastering data stewardship is not just a requirement—it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency. From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You’ll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You’ll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you’ll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship. By the end of this book, you’ll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.What you will learn Enhance your job prospects by understanding the data stewardship field, roles, and responsibilities Discover how to develop a data strategy and translate it into a functional data operating model Develop an effective and efficient data stewardship program Gain practical experience of establishing a data stewardship initiative Implement purposeful governance with measurable ROI Prioritize data use cases with the value and effort matrix Who this book is for This book is for professionals working in the field of data management, including business analysts, data scientists, and data engineers looking to gain a deeper understanding of the data steward role. Senior executives who want to (re)establish the data governance body in their organizations will find this resource invaluable. While accessible to both beginners and professionals, basic knowledge of data management concepts, such as data modeling, data warehousing, and data quality, is a must to get started. |
| cloud data management capabilities: Transportation Systems and Engineering: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2015-06-30 From driverless cars to vehicular networks, recent technological advances are being employed to increase road safety and improve driver satisfaction. As with any newly developed technology, researchers must take care to address all concerns, limitations, and dangers before widespread public adoption. Transportation Systems and Engineering: Concepts, Methodologies, Tools, and Applications addresses current trends in transportation technologies, such as smart cars, green technologies, and infrastructure development. This multivolume book is a critical reference source for engineers, computer scientists, transportation authorities, students, and practitioners in the field of transportation systems management. |
| cloud data management capabilities: IT-CMF – A Management Guide - Based on the IT Capability Maturity FrameworkTM (IT-CMFTM) 2nd edition Declan Kavanagh, Jim Kenneally, Marian Carcary, Martin Curley, 2017-07-27 This management guide offers an introduction to the IT Capability Maturity FrameworkTM (IT-CMFTM), 2nd edition. The IT-CMF offers a comprehensive suite of tried and tested practices, organizational assessment approaches, and improvement roadmaps covering key IT capabilities needed to optimize value and innovation in the IT function and the wider organization. It enables organizations to devise more robust strategies, make better-informed decisions, and perform more effectively, efficiently, and consistently. IT-CMF is: • An integrated management toolkit covering 36 key capability management disciplines, with organizational maturity profiles, assessment methods, and improvement roadmaps for each. • A coherent set of concepts and principles, expressed in business language, that can be used to guide discussions on setting goals and evaluating performance. • A unifying (or umbrella) framework that complements other, domain-specific frameworks already in use in the organization, helping to resolve conflicts between them, and filling gaps in their coverage. • Industry/sector and vendor independent. IT-CMF can be used in any organizational context to guide performance improvement. • A rigorously developed approach, underpinned by the principles of Open Innovation and guided by the Design Science Research methodology, synthesizing leading academic research with industry practitioner expertise ‘IT-CMF provides us with a structured and systematic approach to identify the capabilities we need, a way to assess our strengths and weaknesses, and clear pathways to improve our performance.’ Suresh Kumar, Senior Executive Vice President and Chief Information Officer, BNY Mellon ‘To successfully respond to competitive forces, organizations need to continually review and evolve their existing IT practices, processes, and cultural norms across the entire organization. IT-CMF provides a structured framework for them to do that.’ Christian Morales, Corporate Vice President and General Manager EMEA, Intel Corporation ‘We have successfully applied IT-CMF in over 200 assignments for clients. It just works. Or, as our clients confirm, it helps them create more value from IT.’ Ralf Dreischmeier, Senior Partner and Managing Director, The Boston Consulting Group ‘By using IT-CMF, business leaders can make sure that the tremendous potential of information technology is realized in their organizations.’ Professor Philip Nolan, President, Maynooth University ‘I believe IT-CMF to be comprehensive and credible. Using the framework helps organizations to objectively identify and confirm priorities as the basis for driving improvements.’ Dr Colin Ashurst, Senior Lecturer and Director of Innovation, Newcastle University Business School |
| cloud data management capabilities: T Bytes Digital Customer Experience IT Shades.com, 2020-12-02 This document brings together a set of latest data points and publicly available information relevant for Digital Customer Experience Industry. We are very excited to share this content and believe that readers will benefit from this periodic publication immensely. |
| cloud data management capabilities: Cloud Data Science: Harnessing Azure Machine Learning with Python Peter Jones, 2024-10-15 Unlock the full potential of your data with Cloud Data Science: Harnessing Azure Machine Learning with Python. This comprehensive guide equips you with the knowledge and skills to leverage the power of Azure Machine Learning and the versatility of Python to innovate and streamline your machine learning workflows. From setting up your Azure Machine Learning workspace to deploying sophisticated models, this book covers essential techniques and advanced methodologies in a clear, practical format. Dive into core topics such as data management, automated machine learning workflows, model optimization, and real-time monitoring to ensure your projects are scalable, efficient, and effective. Whether you're a data scientist, machine learning engineer, or a professional seeking to enhance your understanding of cloud-based machine learning, this book offers invaluable insights and hands-on examples to help you transform vast amounts of data into actionable insights. Explore real-world case studies across various industries, learn to overcome common challenges, and discover best practices for implementing machine learning projects successfully. Cloud Data Science: Harnessing Azure Machine Learning with Python is your gateway to mastering data science in the cloud and advancing your professional capabilities in the future of technology. |
| cloud data management capabilities: Introduction to Grid and Cloud Computing Dr. R. Deepalakshmi, Dr. P. Alli, M. Shyni Beaulah, 2017-01-01 This book deals with Anna University Regulation 2013 for the Syllabus CS 6703 Introduction to Grid and Cloud Computing. There are Five units covered in this book. Following are the unit plan of the book. UNIT I INTRODUCTION Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers – Grid computing Infrastructures – cloud computing – service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture. UNIT II GRID SERVICES – Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality Requirements – Practical & Detailed view of OGSA/OGSI – Data intensive grid service models – OGSA services. UNIT III VIRTUALIZATION – Cloud deployment models: public, private, hybrid, community – Categories of cloud computing: Everything as a service: Infrastructure, platform, software – Pros and Cons of cloud computing – Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management – Virtualization for data center automation. UNIT IV PROGRAMMING MODEL – Open source grid middleware packages – Globus Toolkit (GT4) Architecture, Configuration – Usage of Globus – Main components and Programming model – Introduction to Hadoop Framework – Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters, configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and java interface, dataflow of File read & File write. UNIT V SECURITY – Trust models for Grid security environment – Authentication and Authorization methods – Grid security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of data security, provider data and its security, Identity and access management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud. |
| cloud data management capabilities: THE FINTECH HANDBOOK Ashish Srivastava, Sanjeev Jain, Vajha Viharika, 2024-10-11 |
| cloud data management capabilities: Objects and Databases Alan Dearle, Roberto V. Zicari, 2010-09-27 This book constitutes the thoroughly refereed conference proceedings of the Third International Conference on Object Databases, ICOODB 2010, held in Frankfurt/Main, Germany in September 2010. The 10 revised full papers presented together with 4 keynote papers were carefully reviewed and selected from numerous submissions. These papers address a wide range of issues related to object databases, including topics such as linkage to service platforms; operation within scalable (cloud) platforms; object-relational bindings; NoSQL databases; and new approaches to concurrency control. |
| cloud data management capabilities: Cloud Portability and Interoperability Beniamino Di Martino, Giuseppina Cretella, Antonio Esposito, 2015-03-18 This book offers readers a quick, comprehensive and up-to-date overview of the most important methodologies, technologies, APIs and standards related to the portability and interoperability of cloud applications and services, illustrated by a number of use cases representing a variety of interoperability and portability scenarios. The lack of portability and interoperability between cloud platforms at different service levels is the main issue affecting cloud-based services today. The brokering, negotiation, management, monitoring and reconfiguration of cloud resources are challenging tasks for developers and users of cloud applications due to the different business models associated with resource consumption, and to the variety of services and features offered by different cloud providers. In chapter 1 the concepts of cloud portability and interoperability are introduced, together with the issues and limitations arising when such features are lacking or ignored. Subsequently, chapter 2 provides an overview of the state-of-the-art methodologies and technologies that are currently used or being explored to enable cloud portability and interoperability. Chapter 3 illustrates the main cross-platform cloud APIs and how they can solve interoperability and portability issues. In turn, chapter 4 presents a set of ready-to-use solutions which, either because of their broad-scale use in cloud computing scenarios or because they utilize established or emerging standards, play a fundamental part in providing interoperable and portable solutions. Lastly, chapter 5 presents an overview of emerging standards for cloud Interoperability and portability. Researchers and developers of cloud-based services will find here a brief survey of the relevant methodologies, APIs and standards, illustrated by case studies and complemented by an extensive reference list for more detailed descriptions of every topic covered. |
| cloud data management capabilities: Applications of Block Chain technology and Artificial Intelligence Mohammad Irfan, |
| cloud data management capabilities: Emerging Trends in Cloud Computing Analytics, Scalability, and Service Models Darwish, Dina, 2024-01-25 Academic scholars and industry professionals alike face the formidable challenge of staying informed about emerging trends and innovations in cloud computing. The expansive realm of cloud technology has been the catalyst for several transformative changes across industries, offering unparalleled opportunities for optimization and innovation. However, even seasoned experts may find themselves daunted by the intricate web of new technologies, including green cloud computing, edge computing, cryptography in the cloud, load balancing strategies, and cloud analytics insights. Emerging Trends in Cloud Computing: Analytics, Scalability, and Service Models provides academic scholars and industry professionals with a comprehensive exploration of these critical cloud computing topics and more. This invaluable resource provides clarity and insight, serving as a guiding beacon in the ever-evolving world of cloud technology. Whether you're seeking to understand the intricacies of cloud security solutions, the nuances of scalability in cloud computing, or the various service models in the cloud, this book empowers you to navigate this dynamic field with confidence and expertise. |
| cloud data management capabilities: Managing Big Data in Cloud Computing Environments Ma, Zongmin, 2016-02-02 Cloud computing has proven to be a successful paradigm of service-oriented computing, and has revolutionized the way computing infrastructures are abstracted and used. By means of cloud computing technology, massive data can be managed effectively and efficiently to support various aspects of problem solving and decision making. Managing Big Data in Cloud Computing Environments explores the latest advancements in the area of data management and analysis in the cloud. Providing timely, research-based information relating to data storage, sharing, extraction, and indexing in cloud systems, this publication is an ideal reference source for graduate students, IT specialists, researchers, and professionals working in the areas of data and knowledge engineering. |
| cloud data management capabilities: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines. |
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