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| cloud data management capabilities framework: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| cloud data management capabilities framework: 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 framework: 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 framework: 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 framework: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| cloud data management capabilities framework: 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 framework: 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 framework: 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 framework: Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing Management Association, Information Resources, 2021-01-25 Distributed systems intertwine with our everyday lives. The benefits and current shortcomings of the underpinning technologies are experienced by a wide range of people and their smart devices. With the rise of large-scale IoT and similar distributed systems, cloud bursting technologies, and partial outsourcing solutions, private entities are encouraged to increase their efficiency and offer unparalleled availability and reliability to their users. The Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing is a vital reference source that provides valuable insight into current and emergent research occurring within the field of distributed computing. It also presents architectures and service frameworks to achieve highly integrated distributed systems and solutions to integration and efficient management challenges faced by current and future distributed systems. Highlighting a range of topics such as data sharing, wireless sensor networks, and scalability, this multi-volume book is ideally designed for system administrators, integrators, designers, developers, researchers, academicians, and students. |
| cloud data management capabilities framework: 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 framework: Cloud-Based Design and Manufacturing (CBDM) Dirk Schaefer, 2014-06-16 The book introduces the reader to game-changing ways of building and utilizing Internet-based services related to design and manufacture activities through the cloud. In a broader sense, CBDM refers to a new product realization model that enables collective open innovation and rapid product development with minimum costs through social networking and negotiation platforms between service providers and consumers. It is a type of parallel and distributed system consisting of a collection of inter-connected physical and virtualized service pools of design and manufacturing resources as well as intelligent search capabilities for design and manufacturing solutions. Practicing engineers and decision makers will learn how to strategically position their product development operations for success in a globalized interconnected world. |
| cloud data management capabilities framework: 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 framework: Data Fabric Architectures Vandana Sharma, Balamurugan Balusamy, J. Joshua Thomas, L. Godlin Atlas, 2023-05-22 The immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes. |
| cloud data management capabilities framework: 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 framework: Proceedings of the International Conference on Advancing and Redesigning Education 2023 Mohd Fakhizan bin Romlie, |
| cloud data management capabilities framework: Cloud Computing Zaigham Mahmood, 2013-05-16 This book presents both state-of-the-art research developments and practical guidance on approaches, technologies and frameworks for the emerging cloud paradigm. Topics and features: presents the state of the art in cloud technologies, infrastructures, and service delivery and deployment models; discusses relevant theoretical frameworks, practical approaches and suggested methodologies; offers guidance and best practices for the development of cloud-based services and infrastructures, and examines management aspects of cloud computing; reviews consumer perspectives on mobile cloud computing and cloud-based enterprise resource planning; explores software performance testing, open-source cloudware support, and assessment methodologies for modernization, migration and pre-migration; describes emerging new methodologies relevant to the cloud paradigm, and provides suggestions for future developments and research directions. |
| cloud data management capabilities framework: Data Management at Scale Piethein Strengholt, 2023-04-10 As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| cloud data management capabilities framework: 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 framework: 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 framework: 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 framework: Data Analytics and Digital Transformation Erik Beulen, Marla A. Dans, 2023-12-01 Understanding the significance of data analytics is paramount for digital transformation but in many organizations they are separate units without fully aligned goals. As organizations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role in their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organizations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid, and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organizational studies, and strategic management. It is also relevant for policy makers, regulators, and executives of private and public organizations looking to implement successful transformation policies. |
| cloud data management capabilities framework: Advances on Smart and Soft Computing Faisal Saeed, Tawfik Al-Hadhrami, Fathey Mohammed, Errais Mohammed, 2020-10-19 This book gathers high-quality papers presented at the First International Conference of Advanced Computing and Informatics (ICACIn 2020), held in Casablanca, Morocco, on April 12–13, 2020. It covers a range of topics, including artificial intelligence technologies and applications, big data analytics, smart computing, smart cities, Internet of things (IoT), data communication, cloud computing, machine learning algorithms, data stream management and analytics, deep learning, data mining applications, information retrieval, cloud computing platforms, parallel processing, natural language processing, predictive analytics, knowledge management approaches, information security, security in IoT, big data and cloud computing, high-performance computing and computational informatics. |
| cloud data management capabilities framework: Trends in Cloud-based IoT Fadi Al-Turjman, 2020-06-01 This book examines research topics in IoT and Cloud and Fog computing. The contributors address major issues and challenges in IoT-based solutions proposed for the Cloud. The authors discuss Cloud smart and energy efficient services in applications such as healthcare, traffic, and farming systems. Targeted readers are from varying disciplines who are interested in designing and deploying the Cloud applications. The book can be helpful to Cloud-based IoT service providers, Cloud-based IoT service consumers, and Cloud service developers in general for getting the state-of-the-art knowledge in the emerging IoT area. The book also provides a strong foundation for researchers to advance further in this domain. Presents a variety of research related to IoT and Cloud computing; Provides the industry with new and innovative operational ideas; Pertinent to academics, researchers, and practitioners around the world. |
| cloud data management capabilities framework: Cloud Data Center Network Architectures and Technologies Lei Zhang, Le Chen, 2021-04-23 Cloud Data Center Network Architectures and Technologies has been written with the support of Huawei's vast technical knowledge and experience in the data center network (DCN) field, as well as its understanding of customer service requirements. This book describes in detail the architecture design, technical implementation, planning and design, and deployment suggestions for cloud DCNs based on the service challenges DCNs encounter. It starts by describing the overall architecture and technical evolution of DCNs, with the aim of helping readers understand the development of DCNs. It then proceeds to explain the design and implementation of cloud DCNs, including the service model of a single data center (DC), construction of physical and logical networks of DCs, construction of multiple DCNs, and security solutions of DCs. Next, this book dives deep into practices of cloud DCN deployment based on real-world cases to help readers better understand how to build cloud DCNs. Finally, this book introduces DCN openness and some of the hottest forward-looking technologies. In summary, you can use this book as a reference to help you to build secure, reliable, efficient, and open cloud DCNs. It is intended for technical professionals of enterprises, research institutes, information departments, and DCs, as well as teachers and students of computer network-related majors in colleges and universities. Authors Lei Zhang Mr. Zhang is the Chief Architect of Huawei's DCN solution. He has more than 20 years' experience in network product and solution design, as well as a wealth of expertise in product design and development, network planning and design, and network engineering project implementation. He has led the design and deployment of more than 10 large-scale DCNs for Fortune Global 500 companies worldwide. Le Chen Mr. Chen is a Huawei DCN Solution Documentation Engineer with eight years' experience in developing documents related to DCN products and solutions. He has participated in the design and delivery of multiple large-scale enterprise DCNs. Mr. Chen has written many popular technical document series, such as DCN Handbook and BGP Topic. |
| cloud data management capabilities framework: Oracle Essentials Rick Greenwald, Robert Stackowiak, Jonathan Stern, 2013-09-06 Written by Oracle insiders, this indispensable guide distills an enormous amount of information about the Oracle Database into one compact volume. Ideal for novice and experienced DBAs, developers, managers, and users, Oracle Essentials walks you through technologies and features in Oracle’s product line, including its architecture, data structures, networking, concurrency, and tuning. Complete with illustrations and helpful hints, this fifth edition provides a valuable one-stop overview of Oracle Database 12c, including an introduction to Oracle and cloud computing. Oracle Essentials provides the conceptual background you need to understand how Oracle truly works. Topics include: A complete overview of Oracle databases and data stores, and Fusion Middleware products and features Core concepts and structures in Oracle’s architecture, including pluggable databases Oracle objects and the various datatypes Oracle supports System and database management, including Oracle Enterprise Manager 12c Security options, basic auditing capabilities, and options for meeting compliance needs Performance characteristics of disk, memory, and CPU tuning Basic principles of multiuser concurrency Oracle’s online transaction processing (OLTP) Data warehouses, Big Data, and Oracle’s business intelligence tools Backup and recovery, and high availability and failover solutions |
| cloud data management capabilities framework: Cloud Computing – CLOUD 2023 Min Luo, |
| cloud data management capabilities framework: Machine Learning and Data Science Techniques for Effective Government Service Delivery Ogunleye, Olalekan Samuel, 2024-03-27 In our data-rich era, extracting meaningful insights from the vast amount of information has become a crucial challenge, especially in government service delivery where informed decisions are paramount. Traditional approaches struggle with the enormity of data, highlighting the need for a new approach that integrates data science and machine learning. The book, Machine Learning and Data Science Techniques for Effective Government Service Delivery, becomes a vital resource in this transformation, offering a deep understanding of these technologies and their applications. Within the complex landscape of modern governance, this book stands as a solution-oriented guide. Recognizing data's value in the 21st century, it navigates the world of data science and machine learning, enhancing the mechanics of government service. By addressing citizens' evolving needs, these advanced methods counter inefficiencies in traditional systems. Tailored for experts across technology, academia, and government, the book bridges theory and practicality. Covering foundational concepts and innovative applications, it explores the potential of data-driven decision-making for a more efficient and citizen-centric government future. |
| cloud data management capabilities framework: Cloud Technology: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2014-10-31 As the Web grows and expands into ever more remote parts of the world, the availability of resources over the Internet increases exponentially. Making use of this widely prevalent tool, organizations and individuals can share and store knowledge like never before. Cloud Technology: Concepts, Methodologies, Tools, and Applications investigates the latest research in the ubiquitous Web, exploring the use of applications and software that make use of the Internets anytime, anywhere availability. By bringing together research and ideas from across the globe, this publication will be of use to computer engineers, software developers, and end users in business, education, medicine, and more. |
| cloud data management capabilities framework: Oceanobs'19: An Ocean of Opportunity. Volume III Tong Lee, Sabrina Speich, Laura Lorenzoni, Sanae Chiba, Frank E. Muller-Karger, Minhan Dai, Amos T. Kabo-Bah, John Siddorn, Justin Manley, Maria Snoussi, Fei Chai, 2020-12-31 This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact. |
| cloud data management capabilities framework: Web and Big Data Xiangyu Song, |
| cloud data management capabilities framework: Fusion and Integration of Clouds, Edges, and Devices Junlong Zhou, Kun Cao, Jin Sun, Keqin Li, 2024-12-06 This book provides an in-depth examination of recent research advances in cloud-edge-end computing, covering theory, technologies, architectures, methods, applications, and future research directions. It aims to present state-of-the-art models and optimization methods for fusing and integrating clouds, edges, and devices. Cloud-edge-end computing provides users with low-latency, high-reliability, and cost-effective services through the fusion and integration of clouds, edges, and devices. As a result, it is now widely used in various application scenarios. The book introduces the background and fundamental concepts of clouds, edges, and devices, and details the evolution, concepts, enabling technologies, architectures, and implementations of cloud-edge-end computing. It also examines different types of cloud-edge-end orchestrated systems and applications and discusses advanced performance modeling approaches, as well as the latest research on offloading and scheduling policies. It also covers resource management methods for optimizing application performance on cloud-edge-end orchestrated systems. The intended readers of this book are researchers, undergraduate and graduate students, and engineers interested in cloud computing, edge computing, and the Internet of Things. The knowledge of this book will enrich our readers to be at the forefront of cloud-edge-end computing. |
| cloud data management capabilities framework: Enterprise Cloud Strategy Barry Briggs, Eduardo Kassner, 2016-01-07 How do you start? How should you build a plan for cloud migration for your entire portfolio? How will your organization be affected by these changes? This book, based on real-world cloud experiences by enterprise IT teams, seeks to provide the answers to these questions. Here, you’ll see what makes the cloud so compelling to enterprises; with which applications you should start your cloud journey; how your organization will change, and how skill sets will evolve; how to measure progress; how to think about security, compliance, and business buy-in; and how to exploit the ever-growing feature set that the cloud offers to gain strategic and competitive advantage. |
| cloud data management capabilities framework: 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. |
| cloud data management capabilities framework: 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 framework: Big Data: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2016-04-20 The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. Big Data: Concepts, Methodologies, Tools, and Applications is a multi-volume compendium of research-based perspectives and solutions within the realm of large-scale and complex data sets. Taking a multidisciplinary approach, this publication presents exhaustive coverage of crucial topics in the field of big data including diverse applications, storage solutions, analysis techniques, and methods for searching and transferring large data sets, in addition to security issues. Emphasizing essential research in the field of data science, this publication is an ideal reference source for data analysts, IT professionals, researchers, and academics. |
| cloud data management capabilities framework: Communication Infrastructures for Cloud Computing Mouftah, Hussein T., 2013-09-30 Cloud computing has provided multiple advantages as well as challenges to software and infrastructure services. In order to be fully beneficial, these challenges facing cloud specific communication protocols must be addressed. Communication Infrastructures for Cloud Computing presents the issues and research directions for a broad range of cloud computing aspects of software, computing, and storage systems. This book will highlight a broad range of topics in communication infrastructures for cloud computing that will benefit researchers, academics, and practitioners in the active fields of engineering, computer science, and software. |
| cloud data management capabilities framework: Data Governance Dimitrios Sargiotis, |
| cloud data management capabilities framework: IGNOU PGDCA Database Management Systems Previous Years Unsolved Papers Manish Soni, 2024-11-10 This book, IGNOU Database Management Systems Previous Years Unsolved Papers (MCS-207), is a thoughtfully compiled collection of unsolved question papers from previous years. It is designed to serve as an indispensable resource for students preparing for their exams in DBMS. The primary aim of this book is to equip students with a comprehensive tool to self-assess their understanding, pinpoint areas that require further study, and enhance their problem-solving capabilities. |
| cloud data management capabilities framework: CCSP (ISC)2 Certified Cloud Security Professional Exam Guide Omar A. Turner, Navya Lakshmana, 2024-06-21 Become a Certified Cloud Security Professional and open new avenues for growth in your career Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, exam tips, and the eBook PDF Key Features Gain confidence to pass the CCSP exam with tricks, techniques, and mock tests Break down complex technical topics with the help of two experienced CCSP bootcamp educators Learn all you need to know about cloud security to excel in your career beyond the exam Book DescriptionPreparing for the Certified Cloud Security Professional (CCSP) exam can be challenging, as it covers a wide array of topics essential for advancing a cybersecurity professional’s career by validating their technical skills. To prepare for the CCSP exam, you need a resource that not only covers all the exam objectives but also helps you prepare for the format and structure of the exam. Written by two seasoned cybersecurity professionals with a collective experience of hundreds of hours training CCSP bootcamps, this CCSP study guide reflects the journey you’d undertake in such training sessions. The chapters are packed with up-to-date information necessary to pass the (ISC)2 CCSP exam. Additionally, to boost your confidence, the book provides self-assessment questions, exam tips, and mock exams with detailed answer explanations. You’ll be able to deepen your understanding using illustrative explanations that briefly review key points. As you progress, you’ll delve into advanced technical aspects of cloud domain security, such as application security, design, managing and securing data, and infrastructure in the cloud using best practices and legal policies and procedures. By the end of this guide, you’ll be ready to breeze through the exam and tackle real-world cloud security challenges with ease.What you will learn Gain insights into the scope of the CCSP exam and why it is important for your security career Familiarize yourself with core cloud security concepts, architecture, and design principles Analyze cloud risks and prepare for worst-case scenarios Delve into application security, mastering assurance, validation, and verification Explore privacy, legal considerations, and other aspects of the cloud infrastructure Understand the exam registration process, along with valuable practice tests and learning tips Who this book is for This CCSP book is for IT professionals, security analysts, and professionals who want to pursue a career in cloud security, aiming to demonstrate real-world skills. It also caters to existing IT and security professionals looking to acquire practical cloud security expertise and validate their proficiency through the CCSP certification. To get started with this book, a solid understanding of cloud technologies and cybersecurity basics is necessary. |
| cloud data management capabilities framework: 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 Computing Services | Google Cloud
Meet your business challenges head on with cloud computing services from Google, including data management, …
Cloud Storage | Google Cloud
Cloud Storage | Google Cloud
Google Cloud Platform
Google Cloud Platform lets you build, deploy, and scale applications, websites, and services on the same …
Cloud-Computing-Dienste - Google Cloud
Meistern Sie geschäftliche Herausforderungen mit Cloud-Computing-Diensten von Google wie …
Servizi di cloud computing | Google Cloud
Affronta le tue sfide aziendali con i servizi di cloud computing di Google, inclusi gestione dei dati, ambienti ibridi e multi-cloud, AI e machine learning.
Cloud Computing Services | Google Cloud
Meet your business challenges head on with cloud computing services from Google, including data management, hybrid & multi …
Cloud Storage | Google Cloud
Cloud Storage | Google Cloud
Google Cloud Platform
Google Cloud Platform lets you build, deploy, and scale applications, websites, and services on the same infrastructure as Google.
Cloud-Computing-Dienste - Google Cloud
Meistern Sie geschäftliche Herausforderungen mit Cloud-Computing-Diensten von Google wie Datenverwaltung, Hybrid- und Multi-Cloud …
Servizi di cloud computing | Google Cloud
Affronta le tue sfide aziendali con i servizi di cloud computing di Google, inclusi gestione dei dati, ambienti ibridi e multi-cloud, AI e machine learning.
