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| cloud based data management: Cloud Data Management Liang Zhao, Sherif Sakr, Anna Liu, Athman Bouguettaya, 2016-09-03 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 based data management: Cloud Services, Networking, and Management Nelson L. S. da Fonseca, Raouf Boutaba, 2015-04-20 Cloud Services, Networking and Management provides a comprehensive overview of the cloud infrastructure and services, as well as their underlying management mechanisms, including data center virtualization and networking, cloud security and reliability, big data analytics, scientific and commercial applications. Special features of the book include: State-of-the-art content Self-contained chapters for readers with specific interests Includes commercial applications on Cloud (video services and games) |
| cloud based data management: Web-Scale Data Management for the Cloud Wolfgang Lehner, Kai-Uwe Sattler, 2013-04-06 The efficient management of a consistent and integrated database is a central task in modern IT and highly relevant for science and industry. Hardly any critical enterprise solution comes without any functionality for managing data in its different forms. Web-Scale Data Management for the Cloud addresses fundamental challenges posed by the need and desire to provide database functionality in the context of the Database as a Service (DBaaS) paradigm for database outsourcing. This book also discusses the motivation of the new paradigm of cloud computing, and its impact to data outsourcing and service-oriented computing in data-intensive applications. Techniques with respect to the support in the current cloud environments, major challenges, and future trends are covered in the last section of this book. A survey addressing the techniques and special requirements for building database services are provided in this book as well. |
| cloud based data management: Data Management in the Cloud Divyakant Agrawal, Sudipto Das, Amr El Abbadi, 2022-05-31 Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an increase in the scale of the data generated as well as consumed by such applications. Scalable database management systems form a critical part of the cloud infrastructure. The attempt to address the challenges posed by the management of big data has led to a plethora of systems. This book aims to clarify some of the important concepts in the design space of scalable data management in cloud computing infrastructures. Some of the questions that this book aims to answer are: the appropriate systems for a specific set of application requirements, the research challenges in data management for the cloud, and what is novel in the cloud for database researchers? We also aim to address one basic question: whether cloud computing poses new challenges in scalable data management or it is just a reincarnation of old problems? We provide a comprehensive background study of state-of-the-art systems for scalable data management and analysis. We also identify important aspects in the design of different systems and the applicability and scope of these systems. A thorough understanding of current solutions and a precise characterization of the design space are essential for clearing the cloudy skies of data management and ensuring the success of DBMSs in the cloud, thus emulating the success enjoyed by relational databases in traditional enterprise settings. Table of Contents: Introduction / Distributed Data Management / Cloud Data Management: Early Trends / Transactions on Co-located Data / Transactions on Distributed Data / Multi-tenant Database Systems / Concluding Remarks |
| cloud based data management: Smart Grid Technology Sudip Misra, Samaresh Bera, 2018-07-12 Discusses concepts of smart grid technologies, from the perspective of integration with cloud computing and data management approaches. |
| cloud based data management: Advanced Data Management Lena Wiese, 2015-10-29 Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions. This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market. |
| cloud based data management: Cloud Database Development and Management Lee Chao, 2013-07-26 Although today’s job market requires IT professionals to understand cloud computing theories and have hands-on skills for developing real-world database systems, there are few books available that integrate coverage of both. Filling this void, Cloud Database Development and Management explains how readers can take advantage of the cloud environment to develop their own fully functioning database systems without any additional investment in IT infrastructure. Filled with step-by-step instructions, examples, and hands-on projects, the book begins by providing readers with the required foundation in database systems and cloud-based database development tools. It supplies detailed instructions on setting up data storage on Windows Azure and also explains how readers can develop their own virtual machines with Windows Server 2012 as the guest operating system. The book’s wide-ranging coverage includes database design, database implementation, database deployment to the cloud environment, SQL Database, Table Storage service, Blob Storage service, Queue Storage service, and database application development. The text deals with all three aspects of database design: conceptual design, logical design, and physical design. It introduces the SQL language, explains how to use SQL to create database objects, and introduces the migration of the database between Windows Azure and the on-premises SQL Server. It also discusses the management tasks that keep both SQL Database and Windows Azure running smoothly. Detailing how to design, implement, and manage database systems in the cloud, the book provides you with tools that can make your cloud database development much more efficient and flexible. Its easy-to-follow instructions will help you develop the hands-on skills needed to store and manage critical business information and to make that data available anytime through the Internet. |
| cloud based data management: Cloud-Based RDF Data Management Zoi Kaoudi, Ioana Manolescu, Stamatis Zampetakis, 2022-05-31 Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs. Managing such large volumes of RDF data is challenging due to the sheer size, heterogeneity, and complexity brought by RDF reasoning. To tackle the size challenge, distributed architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications requiring distributed architectures for the scalability, fault tolerance, and elasticity features it provides. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment. In this book, we study the state-of-the-art RDF data management in cloud environments and parallel/distributed architectures that were not necessarily intended for the cloud, but can easily be deployed therein. After providing a comprehensive background on RDF and cloud technologies, we explore four aspects that are vital in an RDF data management system: data storage, query processing, query optimization, and reasoning. We conclude the book with a discussion on open problems and future directions. |
| cloud based data management: 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 based data management: In-Memory Data Management Hasso Plattner, Alexander Zeier, 2012-04-17 In the last fifty years the world has been completely transformed through the use of IT. We have now reached a new inflection point. This book presents, for the first time, how in-memory data management is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. This book provides the technical foundation for processing combined transactional and analytical operations in the same database. In the year since we published the first edition of this book, the performance gains enabled by the use of in-memory technology in enterprise applications has truly marked an inflection point in the market. The new content in this second edition focuses on the development of these in-memory enterprise applications, showing how they leverage the capabilities of in-memory technology. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes. |
| cloud based data management: The Informed Company Dave Fowler, Matthew C. David, 2021-10-26 Learn how to manage a modern data stack and get the most out of data in your organization! Thanks to the emergence of new technologies and the explosion of data in recent years, we need new practices for managing and getting value out of data. In the modern, data driven competitive landscape the best guess approach—reading blog posts here and there and patching together data practices without any real visibility—is no longer going to hack it. The Informed Company provides definitive direction on how best to leverage the modern data stack, including cloud computing, columnar storage, cloud ETL tools, and cloud BI tools. You'll learn how to work with Agile methods and set up processes that's right for your company to use your data as a key weapon for your success . . . You'll discover best practices for every stage, from querying production databases at a small startup all the way to setting up data marts for different business lines of an enterprise. In their work at Chartio, authors Fowler and David have learned that most businesspeople are almost completely self-taught when it comes to data. If they are using resources, those resources are outdated, so they're missing out on the latest cloud technologies and advances in data analytics. This book will firm up your understanding of data and bring you into the present with knowledge around what works and what doesn't. Discover the data stack strategies that are working for today's successful small, medium, and enterprise companies Learn the different Agile stages of data organization, and the right one for your team Learn how to maintain Data Lakes and Data Warehouses for effective, accessible data storage Gain the knowledge you need to architect Data Warehouses and Data Marts Understand your business's level of data sophistication and the steps you can take to get to level up your data The Informed Company is the definitive data book for anyone who wants to work faster and more nimbly, armed with actionable decision-making data. |
| cloud based data management: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| cloud based data management: Cloud Network Management Sanjay Kumar Biswash, Sourav Kanti Addya, 2020-10-26 Data storage, processing, and management at remote location over dynamic networks is the most challenging task in cloud networks. Users’ expectations are very high for data accuracy, reliability, accessibility, and availability in pervasive cloud environment. It was the core motivation for the Cloud Networks Internet of Things (CNIoT). The exponential growth of the networks and data management in CNIoT must be implemented in fast growing service sectors such as logistic and enterprise management. The network based IoT works as a bridge to fill the gap between IT and cloud networks, where data is easily accessible and available. This book provides a framework for the next generation of cloud networks, which is the emerging part of 5G partnership projects. This contributed book has following salient features, A cloud-based next generation networking technologies. Cloud-based IoT and mobility management technology. The proposed book is a reference for research scholars and course supplement for cloud-IoT related subjects such as distributed networks in computer/ electrical engineering. Sanjay Kumar Biswash is working as an Assistant professor in NIIT University, India. He held Research Scientist position, Institute of Cybernetics, National Research Tomsk Polytechnic University, Russia. He was PDF at LNCC, Brazil and SDSU, USA. He was a visiting researcher to the UC, Portugal. Sourav Kanti Addya is working as an Assistant professor in NITK, Surathkal, India. He was a PDF at IIT Kharagpur, India. He was a visiting scholar at SDSU, USA. He obtained national level GATE scholarship. He is a member of IEEE, ACM. |
| cloud based data management: Information Storage and Management EMC Education Services, 2012-04-30 The new edition of a bestseller, now revised and update throughout! This new edition of the unparalleled bestseller serves as a full training course all in one and as the world's largest data storage company, EMC is the ideal author for such a critical resource. They cover the components of a storage system and the different storage system models while also offering essential new material that explores the advances in existing technologies and the emergence of the Cloud as well as updates and vital information on new technologies. Features a separate section on emerging area of cloud computing Covers new technologies such as: data de-duplication, unified storage, continuous data protection technology, virtual provisioning, FCoE, flash drives, storage tiering, big data, and more Details storage models such as Network Attached Storage (NAS), Storage Area Network (SAN), Object Based Storage along with virtualization at various infrastructure components Explores Business Continuity and Security in physical and virtualized environment Includes an enhanced Appendix for additional information This authoritative guide is essential for getting up to speed on the newest advances in information storage and management. |
| cloud based data management: Data Management in the Cloud Divyakant Agrawal, Sudipto Das, Amr El Abbadi, 2012-12-01 Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an increase in the scale of the data generated as well as consumed by such applications. Scalable database management systems form a critical part of the cloud infrastructure. The attempt to address the challenges posed by the management of big data has led to a plethora of systems. This book aims to clarify some of the important concepts in the design space of scalable data management in cloud computing infrastructures. Some of the questions that this book aims to answer are: the appropriate systems for a specific set of application requirements, the research challenges in data management for the cloud, and what is novel in the cloud for database researchers? We also aim to address one basic question: whether cloud computing poses new challenges in scalable data management or it is just a reincarnation of old problems? We provide a comprehensive background study of state-of-the-art systems for scalable data management and analysis. We also identify important aspects in the design of different systems and the applicability and scope of these systems. A thorough understanding of current solutions and a precise characterization of the design space are essential for clearing the cloudy skies of data management and ensuring the success of DBMSs in the cloud, thus emulating the success enjoyed by relational databases in traditional enterprise settings. Table of Contents: Introduction / Distributed Data Management / Cloud Data Management: Early Trends / Transactions on Co-located Data / Transactions on Distributed Data / Multi-tenant Database Systems / Concluding Remarks |
| cloud based data management: How to Start a Cloud Based Data Analysis Business AS, How to Start a Business About the Book: Unlock the essential steps to launching and managing a successful business with How to Start a Business books. Part of the acclaimed How to Start a Business series, this volume provides tailored insights and expert advice specific to the industry, helping you navigate the unique challenges and seize the opportunities within this field. What You'll Learn Industry Insights: Understand the market, including key trends, consumer demands, and competitive dynamics. Learn how to conduct market research, analyze data, and identify emerging opportunities for growth that can set your business apart from the competition. Startup Essentials: Develop a comprehensive business plan that outlines your vision, mission, and strategic goals. Learn how to secure the necessary financing through loans, investors, or crowdfunding, and discover best practices for effectively setting up your operation, including choosing the right location, procuring equipment, and hiring a skilled team. Operational Strategies: Master the day-to-day management of your business by implementing efficient processes and systems. Learn techniques for inventory management, staff training, and customer service excellence. Discover effective marketing strategies to attract and retain customers, including digital marketing, social media engagement, and local advertising. Gain insights into financial management, including budgeting, cost control, and pricing strategies to optimize profitability and ensure long-term sustainability. Legal and Compliance: Navigate regulatory requirements and ensure compliance with industry laws through the ideas presented. Why Choose How to Start a Business books? Whether you're wondering how to start a business in the industry or looking to enhance your current operations, How to Start a Business books is your ultimate resource. This book equips you with the knowledge and tools to overcome challenges and achieve long-term success, making it an invaluable part of the How to Start a Business collection. Who Should Read This Book? Aspiring Entrepreneurs: Individuals looking to start their own business. This book offers step-by-step guidance from idea conception to the grand opening, providing the confidence and know-how to get started. Current Business Owners: Entrepreneurs seeking to refine their strategies and expand their presence in the sector. Gain new insights and innovative approaches to enhance your current operations and drive growth. Industry Professionals: Professionals wanting to deepen their understanding of trends and best practices in the business field. Stay ahead in your career by mastering the latest industry developments and operational techniques. Side Income Seekers: Individuals looking for the knowledge to make extra income through a business venture. Learn how to efficiently manage a part-time business that complements your primary source of income and leverages your skills and interests. Start Your Journey Today! Empower yourself with the insights and strategies needed to build and sustain a thriving business. Whether driven by passion or opportunity, How to Start a Business offers the roadmap to turning your entrepreneurial dreams into reality. Download your copy now and take the first step towards becoming a successful entrepreneur! Discover more titles in the How to Start a Business series: Explore our other volumes, each focusing on different fields, to gain comprehensive knowledge and succeed in your chosen industry. |
| cloud based data management: Cloud Data Centers and Cost Modeling Caesar Wu, Rajkumar Buyya, 2015-02-27 Cloud Data Centers and Cost Modeling establishes a framework for strategic decision-makers to facilitate the development of cloud data centers. Just as building a house requires a clear understanding of the blueprints, architecture, and costs of the project; building a cloud-based data center requires similar knowledge. The authors take a theoretical and practical approach, starting with the key questions to help uncover needs and clarify project scope. They then demonstrate probability tools to test and support decisions, and provide processes that resolve key issues. After laying a foundation of cloud concepts and definitions, the book addresses data center creation, infrastructure development, cost modeling, and simulations in decision-making, each part building on the previous. In this way the authors bridge technology, management, and infrastructure as a service, in one complete guide to data centers that facilitates educated decision making. - Explains how to balance cloud computing functionality with data center efficiency - Covers key requirements for power management, cooling, server planning, virtualization, and storage management - Describes advanced methods for modeling cloud computing cost including Real Option Theory and Monte Carlo Simulations - Blends theoretical and practical discussions with insights for developers, consultants, and analysts considering data center development |
| cloud based data management: Enabling the New Era of Cloud Computing: Data Security, Transfer, and Management Shen, Yushi, 2013-11-30 Cloud computing is becoming the next revolution in the IT industry; providing central storage for internet data and services that have the potential to bring data transmission performance, security and privacy, data deluge, and inefficient architecture to the next level. Enabling the New Era of Cloud Computing: Data Security, Transfer, and Management discusses cloud computing as an emerging technology and its critical role in the IT industry upgrade and economic development in the future. This book is an essential resource for business decision makers, technology investors, architects and engineers, and cloud consumers interested in the cloud computing future. |
| cloud based data management: Design Patterns for Cloud Native Applications Kasun Indrasiri, Sriskandarajah Suhothayan, 2021-05-17 With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development. You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step. Learn the fundamentals of cloud native applications Explore key cloud native communication, connectivity, and composition patterns Learn decentralized data management techniques Use event-driven architecture to build distributed and scalable cloud native applications Explore the most commonly used patterns for API management and consumption Examine some of the tools and technologies you'll need for building cloud native systems |
| cloud based data management: 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 based data management: Integrating AI in IoT Analytics on the Cloud for Healthcare Applications Jeya Mala, D., 2022-01-07 Internet of things (IoT) applications employed for healthcare generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution is ideal in order to generate faster reports in comparison to the traditional way. Given the current state of the world in which every day IoT devices are developed to provide healthcare solutions, it is essential to consider the mechanisms used to collect and analyze the data to provide thorough reports. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications applies artificial intelligence (AI) in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and discusses security solutions for edge analytics in healthcare IoT. Covering topics such as data analytics and next generation healthcare systems, it is ideal for researchers, academicians, technologists, IT specialists, data scientists, healthcare industries, IoT developers, data security analysts, educators, and students. |
| cloud based data management: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-04-20 Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. |
| cloud based data management: Data Management Technologies and Applications Slimane Hammoudi, Christoph Quix, Jorge Bernardino, 2021-07-22 This book constitutes the thoroughly refereed proceedings of the 9th International Conference on Data Management Technologies and Applications, DATA 2020, which was supposed to take place in Paris, France, in July 2020. Due to the Covid-19 pandemic the event was held virtually. The 14 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: datamining; decision support systems; data analytics; data and information quality; digital rights management; big data; knowledge management; ontology engineering; digital libraries; mobile databases; object-oriented database systems; data integrity. |
| cloud based data management: 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 based data management: Collection Management in the Cloud Kayla Kipps, Allison Kaiser Jones, 2022-03-11 As remote work has become routine, cloud-based technology tools have become increasingly necessary to communicate with other library staff and with faculty and staff to continue providing seamless and uninterrupted access to library resources and collections for our campus community. Cloud-based technology tools such as Google Forms and Google Sheets are used to gather faculty requests for collection development, tools such as Tableau are used to illustrate material budget balances, and platforms such as Trello have been adopted to track subscription renewal cycles and manage other projects. This guide discusses the benefits of using these powerful cloud-based and little to no additional cost technology tools through the lens of a particular area in librarianship such as documentation, data and project management, communication, data storage, and data visualization. While the real-world examples provided throughout focus on technical services staff operations, specifically acquisitions and electronic collection management, each tool’s features and use cases are transferable among all areas of librarianship. This guide provides insights into how collaborative, dynamic, and accessible these cloud-based solutions are for a technologically shifting workplace as well as considers the challenges to adopting cloud-based solutions such as administrative buy-in, aversion to change, and steeper learning curves as well. Readers will gain practical experiential examples that have been instrumental in creating efficiencies in collection management workflows for technical services staff. The use cases illustrated exemplify enhancements that librarians can incorporate into their own collection management practices to further engage with their colleagues, their patrons, and their larger communities more effectively and efficiently. |
| cloud based data management: Impacts and Challenges of Cloud Business Intelligence Aljawarneh, Shadi, Malhotra, Manisha, 2020-12-18 Cloud computing provides an easier alternative for starting an IT-based business organization that requires much less of an initial investment. Cloud computing offers a significant edge of traditional computing with big data being continuously transferred to the cloud. For extraction of relevant data, cloud business intelligence must be utilized. Cloud-based tools, such as customer relationship management (CRM), Salesforce, and Dropbox are increasingly being integrated by enterprises looking to increase their agility and efficiency. Impacts and Challenges of Cloud Business Intelligence is a cutting-edge scholarly resource that provides comprehensive research on business intelligence in cloud computing and explores its applications in conjunction with other tools. Highlighting a wide range of topics including swarm intelligence, algorithms, and cloud analytics, this book is essential for entrepreneurs, IT professionals, managers, business professionals, practitioners, researchers, academicians, and students. |
| cloud based data management: 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 based data management: 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 based data management: Big Data Management Fausto Pedro García Márquez, Benjamin Lev, 2016-11-15 This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples. |
| cloud based data management: Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing Velayutham, Sathiyamoorthi, 2021-01-29 In today’s market, emerging technologies are continually assisting in common workplace practices as companies and organizations search for innovative ways to solve modern issues that arise. Prevalent applications including internet of things, big data, and cloud computing all have noteworthy benefits, but issues remain when separately integrating them into the professional practices. Significant research is needed on converging these systems and leveraging each of their advantages in order to find solutions to real-time problems that still exist. Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing is a pivotal reference source that provides vital research on the relation between these technologies and the impact they collectively have in solving real-world challenges. While highlighting topics such as cloud-based analytics, intelligent algorithms, and information security, this publication explores current issues that remain when attempting to implement these systems as well as the specific applications IoT, big data, and cloud computing have in various professional sectors. This book is ideally designed for academicians, researchers, developers, computer scientists, IT professionals, practitioners, scholars, students, and engineers seeking research on the integration of emerging technologies to solve modern societal issues. |
| cloud based data management: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| cloud based data management: Big Data Balamurugan Balusamy, Nandhini Abirami R, Seifedine Kadry, Amir H. Gandomi, 2021-04-13 Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book. |
| cloud based data management: 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 based data management: Practical Cloud Security Chris Dotson, 2019-03-04 With their rapidly changing architecture and API-driven automation, cloud platforms come with unique security challenges and opportunities. This hands-on book guides you through security best practices for multivendor cloud environments, whether your company plans to move legacy on-premises projects to the cloud or build a new infrastructure from the ground up. Developers, IT architects, and security professionals will learn cloud-specific techniques for securing popular cloud platforms such as Amazon Web Services, Microsoft Azure, and IBM Cloud. Chris Dotson—an IBM senior technical staff member—shows you how to establish data asset management, identity and access management, vulnerability management, network security, and incident response in your cloud environment. |
| cloud based data management: Cloud Computing Christian Baun, Marcel Kunze, Jens Nimis, Stefan Tai, 2011-07-14 Cloud computing is a buzz-word in today’s information technology (IT) that nobody can escape. But what is really behind it? There are many interpretations of this term, but no standardized or even uniform definition. Instead, as a result of the multi-faceted viewpoints and the diverse interests expressed by the various stakeholders, cloud computing is perceived as a rather fuzzy concept. With this book, the authors deliver an overview of cloud computing architecture, services, and applications. Their aim is to bring readers up to date on this technology and thus to provide a common basis for discussion, new research, and novel application scenarios. They first introduce the foundation of cloud computing with its basic technologies, such as virtualization and Web services. After that they discuss the cloud architecture and its service modules. The following chapters then cover selected commercial cloud offerings (including Amazon Web Services and Google App Engine) and management tools, and present current related open-source developments (including Hadoop, Eucalyptus, and Open CirrusTM). Next, economic considerations (cost and business models) are discussed, and an evaluation of the cloud market situation is given. Finally, the appendix contains some practical examples of how to use cloud resources or cloud applications, and a glossary provides concise definitions of key terms. The authors’ presentation does not require in-depth technical knowledge. It is equally intended as an introduction for students in software engineering, web technologies, or business development, for professional software developers or system architects, and for future-oriented decision-makers like top executives and managers. |
| cloud based data management: Data-Driven Modelling and Predictive Analytics in Business and Finance Alex Khang, Rashmi Gujrati, Hayri Uygun, R. K. Tailor, Sanjaya Gaur, 2024-07-24 Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices. |
| cloud based data management: Agile Data Warehouse Design Lawrence Corr, Jim Stagnitto, 2011-11 Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. This book describes BEAM✲, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM✲ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: ✲ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM✲) ✲ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! ✲ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) ✲ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail ✲ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development ✲ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply ✲ Agile design documentation: enhancing star schemas with BEAM✲ dimensional shorthand notation ✲ Solving difficult DW/BI performance and usability problems with proven dimensional design patterns Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino. |
| cloud based data management: Managing and Processing Big Data in Cloud Computing Kannan, Rajkumar, 2016-01-07 Big data has presented a number of opportunities across industries. With these opportunities come a number of challenges associated with handling, analyzing, and storing large data sets. One solution to this challenge is cloud computing, which supports a massive storage and computation facility in order to accommodate big data processing. Managing and Processing Big Data in Cloud Computing explores the challenges of supporting big data processing and cloud-based platforms as a proposed solution. Emphasizing a number of crucial topics such as data analytics, wireless networks, mobile clouds, and machine learning, this publication meets the research needs of data analysts, IT professionals, researchers, graduate students, and educators in the areas of data science, computer programming, and IT development. |
| cloud based data management: Cloud Computing Service and Deployment Models: Layers and Management Bento, Al, 2012-10-31 This book presents a collection of diverse perspectives on cloud computing and its vital role in all components of organizations, improving the understanding of cloud computing and tackling related concerns such as change management, security, processing approaches, and much more--Provided by publisher. |
| cloud based data management: Measuring the Business Value of Cloud Computing Theo Lynn, John G. Mooney, Pierangelo Rosati, Grace Fox, 2020-08-27 The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. Recent reviews of extant literature highlights the need for multi-disciplinary research. This research should explore and further develops the conceptualization of value in cloud computing research. In addition, there is a need for research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios. This open access book will review the state of the art from an IS, Computer Science and Accounting perspective, will introduce and discuss the main techniques for measuring business value for cloud computing in a variety of scenarios, and illustrate these with mini-case studies. |
| cloud-based data management: Cloud Data Management Liang Zhao, Sherif Sakr, Anna Liu, Athman Bouguettaya, 2016-09-03 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-based data management: Web-Scale Data Management for the Cloud Wolfgang Lehner, Kai-Uwe Sattler, 2013-04-06 The efficient management of a consistent and integrated database is a central task in modern IT and highly relevant for science and industry. Hardly any critical enterprise solution comes without any functionality for managing data in its different forms. Web-Scale Data Management for the Cloud addresses fundamental challenges posed by the need and desire to provide database functionality in the context of the Database as a Service (DBaaS) paradigm for database outsourcing. This book also discusses the motivation of the new paradigm of cloud computing, and its impact to data outsourcing and service-oriented computing in data-intensive applications. Techniques with respect to the support in the current cloud environments, major challenges, and future trends are covered in the last section of this book. A survey addressing the techniques and special requirements for building database services are provided in this book as well. |
| cloud-based data management: Data Management in the Cloud Divyakant Agrawal, Sudipto Das, Amr El Abbadi, 2022-05-31 Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an increase in the scale of the data generated as well as consumed by such applications. Scalable database management systems form a critical part of the cloud infrastructure. The attempt to address the challenges posed by the management of big data has led to a plethora of systems. This book aims to clarify some of the important concepts in the design space of scalable data management in cloud computing infrastructures. Some of the questions that this book aims to answer are: the appropriate systems for a specific set of application requirements, the research challenges in data management for the cloud, and what is novel in the cloud for database researchers? We also aim to address one basic question: whether cloud computing poses new challenges in scalable data management or it is just a reincarnation of old problems? We provide a comprehensive background study of state-of-the-art systems for scalable data management and analysis. We also identify important aspects in the design of different systems and the applicability and scope of these systems. A thorough understanding of current solutions and a precise characterization of the design space are essential for clearing the cloudy skies of data management and ensuring the success of DBMSs in the cloud, thus emulating the success enjoyed by relational databases in traditional enterprise settings. Table of Contents: Introduction / Distributed Data Management / Cloud Data Management: Early Trends / Transactions on Co-located Data / Transactions on Distributed Data / Multi-tenant Database Systems / Concluding Remarks |
| cloud-based data management: Smart Grid Technology Sudip Misra, Samaresh Bera, 2018-07-12 Discusses concepts of smart grid technologies, from the perspective of integration with cloud computing and data management approaches. |
| cloud-based data management: Cloud Database Development and Management Lee Chao, 2013-07-26 Although today’s job market requires IT professionals to understand cloud computing theories and have hands-on skills for developing real-world database systems, there are few books available that integrate coverage of both. Filling this void, Cloud Database Development and Management explains how readers can take advantage of the cloud environment to develop their own fully functioning database systems without any additional investment in IT infrastructure. Filled with step-by-step instructions, examples, and hands-on projects, the book begins by providing readers with the required foundation in database systems and cloud-based database development tools. It supplies detailed instructions on setting up data storage on Windows Azure and also explains how readers can develop their own virtual machines with Windows Server 2012 as the guest operating system. The book’s wide-ranging coverage includes database design, database implementation, database deployment to the cloud environment, SQL Database, Table Storage service, Blob Storage service, Queue Storage service, and database application development. The text deals with all three aspects of database design: conceptual design, logical design, and physical design. It introduces the SQL language, explains how to use SQL to create database objects, and introduces the migration of the database between Windows Azure and the on-premises SQL Server. It also discusses the management tasks that keep both SQL Database and Windows Azure running smoothly. Detailing how to design, implement, and manage database systems in the cloud, the book provides you with tools that can make your cloud database development much more efficient and flexible. Its easy-to-follow instructions will help you develop the hands-on skills needed to store and manage critical business information and to make that data available anytime through the Internet. |
| cloud-based data management: Cloud-Based RDF Data Management Zoi Kaoudi, Ioana Manolescu, Stamatis Zampetakis, 2022-05-31 Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs. Managing such large volumes of RDF data is challenging due to the sheer size, heterogeneity, and complexity brought by RDF reasoning. To tackle the size challenge, distributed architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications requiring distributed architectures for the scalability, fault tolerance, and elasticity features it provides. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment. In this book, we study the state-of-the-art RDF data management in cloud environments and parallel/distributed architectures that were not necessarily intended for the cloud, but can easily be deployed therein. After providing a comprehensive background on RDF and cloud technologies, we explore four aspects that are vital in an RDF data management system: data storage, query processing, query optimization, and reasoning. We conclude the book with a discussion on open problems and future directions. |
| cloud-based data management: 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-based data management: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| cloud-based data management: The Informed Company Dave Fowler, Matthew C. David, 2021-10-26 Learn how to manage a modern data stack and get the most out of data in your organization! Thanks to the emergence of new technologies and the explosion of data in recent years, we need new practices for managing and getting value out of data. In the modern, data driven competitive landscape the best guess approach—reading blog posts here and there and patching together data practices without any real visibility—is no longer going to hack it. The Informed Company provides definitive direction on how best to leverage the modern data stack, including cloud computing, columnar storage, cloud ETL tools, and cloud BI tools. You'll learn how to work with Agile methods and set up processes that's right for your company to use your data as a key weapon for your success . . . You'll discover best practices for every stage, from querying production databases at a small startup all the way to setting up data marts for different business lines of an enterprise. In their work at Chartio, authors Fowler and David have learned that most businesspeople are almost completely self-taught when it comes to data. If they are using resources, those resources are outdated, so they're missing out on the latest cloud technologies and advances in data analytics. This book will firm up your understanding of data and bring you into the present with knowledge around what works and what doesn't. Discover the data stack strategies that are working for today's successful small, medium, and enterprise companies Learn the different Agile stages of data organization, and the right one for your team Learn how to maintain Data Lakes and Data Warehouses for effective, accessible data storage Gain the knowledge you need to architect Data Warehouses and Data Marts Understand your business's level of data sophistication and the steps you can take to get to level up your data The Informed Company is the definitive data book for anyone who wants to work faster and more nimbly, armed with actionable decision-making data. |
| cloud-based data management: Information Storage and Management EMC Education Services, 2012-04-30 The new edition of a bestseller, now revised and update throughout! This new edition of the unparalleled bestseller serves as a full training course all in one and as the world's largest data storage company, EMC is the ideal author for such a critical resource. They cover the components of a storage system and the different storage system models while also offering essential new material that explores the advances in existing technologies and the emergence of the Cloud as well as updates and vital information on new technologies. Features a separate section on emerging area of cloud computing Covers new technologies such as: data de-duplication, unified storage, continuous data protection technology, virtual provisioning, FCoE, flash drives, storage tiering, big data, and more Details storage models such as Network Attached Storage (NAS), Storage Area Network (SAN), Object Based Storage along with virtualization at various infrastructure components Explores Business Continuity and Security in physical and virtualized environment Includes an enhanced Appendix for additional information This authoritative guide is essential for getting up to speed on the newest advances in information storage and management. |
| cloud-based data management: Cloud Network Management Sanjay Kumar Biswash, Sourav Kanti Addya, 2020-10-26 Data storage, processing, and management at remote location over dynamic networks is the most challenging task in cloud networks. Users’ expectations are very high for data accuracy, reliability, accessibility, and availability in pervasive cloud environment. It was the core motivation for the Cloud Networks Internet of Things (CNIoT). The exponential growth of the networks and data management in CNIoT must be implemented in fast growing service sectors such as logistic and enterprise management. The network based IoT works as a bridge to fill the gap between IT and cloud networks, where data is easily accessible and available. This book provides a framework for the next generation of cloud networks, which is the emerging part of 5G partnership projects. This contributed book has following salient features, A cloud-based next generation networking technologies. Cloud-based IoT and mobility management technology. The proposed book is a reference for research scholars and course supplement for cloud-IoT related subjects such as distributed networks in computer/ electrical engineering. Sanjay Kumar Biswash is working as an Assistant professor in NIIT University, India. He held Research Scientist position, Institute of Cybernetics, National Research Tomsk Polytechnic University, Russia. He was PDF at LNCC, Brazil and SDSU, USA. He was a visiting researcher to the UC, Portugal. Sourav Kanti Addya is working as an Assistant professor in NITK, Surathkal, India. He was a PDF at IIT Kharagpur, India. He was a visiting scholar at SDSU, USA. He obtained national level GATE scholarship. He is a member of IEEE, ACM. |
| cloud-based data management: Data Management Technologies and Applications Slimane Hammoudi, Christoph Quix, Jorge Bernardino, 2021-07-22 This book constitutes the thoroughly refereed proceedings of the 9th International Conference on Data Management Technologies and Applications, DATA 2020, which was supposed to take place in Paris, France, in July 2020. Due to the Covid-19 pandemic the event was held virtually. The 14 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: datamining; decision support systems; data analytics; data and information quality; digital rights management; big data; knowledge management; ontology engineering; digital libraries; mobile databases; object-oriented database systems; data integrity. |
| cloud-based data management: Data Management in the Cloud Divyakant Agrawal, Sudipto Das, Amr El Abbadi, 2012-12-01 Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an increase in the scale of the data generated as well as consumed by such applications. Scalable database management systems form a critical part of the cloud infrastructure. The attempt to address the challenges posed by the management of big data has led to a plethora of systems. This book aims to clarify some of the important concepts in the design space of scalable data management in cloud computing infrastructures. Some of the questions that this book aims to answer are: the appropriate systems for a specific set of application requirements, the research challenges in data management for the cloud, and what is novel in the cloud for database researchers? We also aim to address one basic question: whether cloud computing poses new challenges in scalable data management or it is just a reincarnation of old problems? We provide a comprehensive background study of state-of-the-art systems for scalable data management and analysis. We also identify important aspects in the design of different systems and the applicability and scope of these systems. A thorough understanding of current solutions and a precise characterization of the design space are essential for clearing the cloudy skies of data management and ensuring the success of DBMSs in the cloud, thus emulating the success enjoyed by relational databases in traditional enterprise settings. Table of Contents: Introduction / Distributed Data Management / Cloud Data Management: Early Trends / Transactions on Co-located Data / Transactions on Distributed Data / Multi-tenant Database Systems / Concluding Remarks |
| cloud-based data management: How to Start a Cloud Based Data Analysis Business AS, How to Start a Business About the Book: Unlock the essential steps to launching and managing a successful business with How to Start a Business books. Part of the acclaimed How to Start a Business series, this volume provides tailored insights and expert advice specific to the industry, helping you navigate the unique challenges and seize the opportunities within this field. What You'll Learn Industry Insights: Understand the market, including key trends, consumer demands, and competitive dynamics. Learn how to conduct market research, analyze data, and identify emerging opportunities for growth that can set your business apart from the competition. Startup Essentials: Develop a comprehensive business plan that outlines your vision, mission, and strategic goals. Learn how to secure the necessary financing through loans, investors, or crowdfunding, and discover best practices for effectively setting up your operation, including choosing the right location, procuring equipment, and hiring a skilled team. Operational Strategies: Master the day-to-day management of your business by implementing efficient processes and systems. Learn techniques for inventory management, staff training, and customer service excellence. Discover effective marketing strategies to attract and retain customers, including digital marketing, social media engagement, and local advertising. Gain insights into financial management, including budgeting, cost control, and pricing strategies to optimize profitability and ensure long-term sustainability. Legal and Compliance: Navigate regulatory requirements and ensure compliance with industry laws through the ideas presented. Why Choose How to Start a Business books? Whether you're wondering how to start a business in the industry or looking to enhance your current operations, How to Start a Business books is your ultimate resource. This book equips you with the knowledge and tools to overcome challenges and achieve long-term success, making it an invaluable part of the How to Start a Business collection. Who Should Read This Book? Aspiring Entrepreneurs: Individuals looking to start their own business. This book offers step-by-step guidance from idea conception to the grand opening, providing the confidence and know-how to get started. Current Business Owners: Entrepreneurs seeking to refine their strategies and expand their presence in the sector. Gain new insights and innovative approaches to enhance your current operations and drive growth. Industry Professionals: Professionals wanting to deepen their understanding of trends and best practices in the business field. Stay ahead in your career by mastering the latest industry developments and operational techniques. Side Income Seekers: Individuals looking for the knowledge to make extra income through a business venture. Learn how to efficiently manage a part-time business that complements your primary source of income and leverages your skills and interests. Start Your Journey Today! Empower yourself with the insights and strategies needed to build and sustain a thriving business. Whether driven by passion or opportunity, How to Start a Business offers the roadmap to turning your entrepreneurial dreams into reality. Download your copy now and take the first step towards becoming a successful entrepreneur! Discover more titles in the How to Start a Business series: Explore our other volumes, each focusing on different fields, to gain comprehensive knowledge and succeed in your chosen industry. |
| cloud-based data management: Cloud Data Centers and Cost Modeling Caesar Wu, Rajkumar Buyya, 2015-02-27 Cloud Data Centers and Cost Modeling establishes a framework for strategic decision-makers to facilitate the development of cloud data centers. Just as building a house requires a clear understanding of the blueprints, architecture, and costs of the project; building a cloud-based data center requires similar knowledge. The authors take a theoretical and practical approach, starting with the key questions to help uncover needs and clarify project scope. They then demonstrate probability tools to test and support decisions, and provide processes that resolve key issues. After laying a foundation of cloud concepts and definitions, the book addresses data center creation, infrastructure development, cost modeling, and simulations in decision-making, each part building on the previous. In this way the authors bridge technology, management, and infrastructure as a service, in one complete guide to data centers that facilitates educated decision making. - Explains how to balance cloud computing functionality with data center efficiency - Covers key requirements for power management, cooling, server planning, virtualization, and storage management - Describes advanced methods for modeling cloud computing cost including Real Option Theory and Monte Carlo Simulations - Blends theoretical and practical discussions with insights for developers, consultants, and analysts considering data center development |
| cloud-based data management: Design Patterns for Cloud Native Applications Kasun Indrasiri, Sriskandarajah Suhothayan, 2021-05-17 With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development. You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step. Learn the fundamentals of cloud native applications Explore key cloud native communication, connectivity, and composition patterns Learn decentralized data management techniques Use event-driven architecture to build distributed and scalable cloud native applications Explore the most commonly used patterns for API management and consumption Examine some of the tools and technologies you'll need for building cloud native systems |
| cloud-based data management: 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-based data management: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-04-20 Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. |
| cloud-based data management: Integrating AI in IoT Analytics on the Cloud for Healthcare Applications Jeya Mala, D., 2022-01-07 Internet of things (IoT) applications employed for healthcare generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution is ideal in order to generate faster reports in comparison to the traditional way. Given the current state of the world in which every day IoT devices are developed to provide healthcare solutions, it is essential to consider the mechanisms used to collect and analyze the data to provide thorough reports. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications applies artificial intelligence (AI) in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and discusses security solutions for edge analytics in healthcare IoT. Covering topics such as data analytics and next generation healthcare systems, it is ideal for researchers, academicians, technologists, IT specialists, data scientists, healthcare industries, IoT developers, data security analysts, educators, and students. |
| cloud-based data management: Secure and Trust Computing, Data Management, and Applications Changhoon Lee, Jean-Marc Seigneur, James J. (Jong Hyuk) Park, Roland R. Wagner, 2011-06-28 This book constitutes the refereed proceedings of two workshops held in conjunction with the 8th FIRA International Conference on Secure and Trust Computing, Data Management, and Applications, STA 2011, in Crete, Greece, in June 2011. STA 2011 is the first conference after the merger of the successful SSDU, UbiSec, and TRUST symposium series previously held from 2006 until 2010 in various locations. The 14 full papers of the IWCS 2011 and 10 papers of the STAVE 2011 workshop were carefully reviewed and individually selected from the lectures given at each workshop. The International Workshop on Convergence Security in Pervasive Environments, IWCS 2011, addresses the various theories and practical applications of convergence security in pervasive environments. The International Workshop on Security & Trust for Applications in Virtualized Environments, STAVE 2011, shows how current virtualization increases the sharing of compute, network and I/O resources with multiple users and applications in order to drive higher utilization rates, what replaces the traditional physical isolation boundaries with virtual ones. |
| cloud-based data management: Impacts and Challenges of Cloud Business Intelligence Aljawarneh, Shadi, Malhotra, Manisha, 2020-12-18 Cloud computing provides an easier alternative for starting an IT-based business organization that requires much less of an initial investment. Cloud computing offers a significant edge of traditional computing with big data being continuously transferred to the cloud. For extraction of relevant data, cloud business intelligence must be utilized. Cloud-based tools, such as customer relationship management (CRM), Salesforce, and Dropbox are increasingly being integrated by enterprises looking to increase their agility and efficiency. Impacts and Challenges of Cloud Business Intelligence is a cutting-edge scholarly resource that provides comprehensive research on business intelligence in cloud computing and explores its applications in conjunction with other tools. Highlighting a wide range of topics including swarm intelligence, algorithms, and cloud analytics, this book is essential for entrepreneurs, IT professionals, managers, business professionals, practitioners, researchers, academicians, and students. |
| cloud-based data management: Collection Management in the Cloud Kayla Kipps, Allison Kaiser Jones, 2022-03-11 As remote work has become routine, cloud-based technology tools have become increasingly necessary to communicate with other library staff and with faculty and staff to continue providing seamless and uninterrupted access to library resources and collections for our campus community. Cloud-based technology tools such as Google Forms and Google Sheets are used to gather faculty requests for collection development, tools such as Tableau are used to illustrate material budget balances, and platforms such as Trello have been adopted to track subscription renewal cycles and manage other projects. This guide discusses the benefits of using these powerful cloud-based and little to no additional cost technology tools through the lens of a particular area in librarianship such as documentation, data and project management, communication, data storage, and data visualization. While the real-world examples provided throughout focus on technical services staff operations, specifically acquisitions and electronic collection management, each tool’s features and use cases are transferable among all areas of librarianship. This guide provides insights into how collaborative, dynamic, and accessible these cloud-based solutions are for a technologically shifting workplace as well as considers the challenges to adopting cloud-based solutions such as administrative buy-in, aversion to change, and steeper learning curves as well. Readers will gain practical experiential examples that have been instrumental in creating efficiencies in collection management workflows for technical services staff. The use cases illustrated exemplify enhancements that librarians can incorporate into their own collection management practices to further engage with their colleagues, their patrons, and their larger communities more effectively and efficiently. |
| cloud-based data management: 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-based data management: 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-based data management: Big Data Management Fausto Pedro García Márquez, Benjamin Lev, 2016-11-15 This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples. |
| cloud-based data management: Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing Velayutham, Sathiyamoorthi, 2021-01-29 In today’s market, emerging technologies are continually assisting in common workplace practices as companies and organizations search for innovative ways to solve modern issues that arise. Prevalent applications including internet of things, big data, and cloud computing all have noteworthy benefits, but issues remain when separately integrating them into the professional practices. Significant research is needed on converging these systems and leveraging each of their advantages in order to find solutions to real-time problems that still exist. Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing is a pivotal reference source that provides vital research on the relation between these technologies and the impact they collectively have in solving real-world challenges. While highlighting topics such as cloud-based analytics, intelligent algorithms, and information security, this publication explores current issues that remain when attempting to implement these systems as well as the specific applications IoT, big data, and cloud computing have in various professional sectors. This book is ideally designed for academicians, researchers, developers, computer scientists, IT professionals, practitioners, scholars, students, and engineers seeking research on the integration of emerging technologies to solve modern societal issues. |
| cloud-based data management: Big Data Balamurugan Balusamy, Nandhini Abirami R, Seifedine Kadry, Amir H. Gandomi, 2021-04-13 Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book. |
| cloud-based data management: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| cloud-based data management: 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-based data management: 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-based data management: Data Management, Analytics and Innovation Neha Sharma, Amlan Chakrabarti, Valentina Emilia Balas, 2019-10-24 This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. |
| cloud-based data management: Cloud Computing Christian Baun, Marcel Kunze, Jens Nimis, Stefan Tai, 2011-07-14 Cloud computing is a buzz-word in today’s information technology (IT) that nobody can escape. But what is really behind it? There are many interpretations of this term, but no standardized or even uniform definition. Instead, as a result of the multi-faceted viewpoints and the diverse interests expressed by the various stakeholders, cloud computing is perceived as a rather fuzzy concept. With this book, the authors deliver an overview of cloud computing architecture, services, and applications. Their aim is to bring readers up to date on this technology and thus to provide a common basis for discussion, new research, and novel application scenarios. They first introduce the foundation of cloud computing with its basic technologies, such as virtualization and Web services. After that they discuss the cloud architecture and its service modules. The following chapters then cover selected commercial cloud offerings (including Amazon Web Services and Google App Engine) and management tools, and present current related open-source developments (including Hadoop, Eucalyptus, and Open CirrusTM). Next, economic considerations (cost and business models) are discussed, and an evaluation of the cloud market situation is given. Finally, the appendix contains some practical examples of how to use cloud resources or cloud applications, and a glossary provides concise definitions of key terms. The authors’ presentation does not require in-depth technical knowledge. It is equally intended as an introduction for students in software engineering, web technologies, or business development, for professional software developers or system architects, and for future-oriented decision-makers like top executives and managers. |
| cloud-based data management: Managing and Processing Big Data in Cloud Computing Kannan, Rajkumar, 2016-01-07 Big data has presented a number of opportunities across industries. With these opportunities come a number of challenges associated with handling, analyzing, and storing large data sets. One solution to this challenge is cloud computing, which supports a massive storage and computation facility in order to accommodate big data processing. Managing and Processing Big Data in Cloud Computing explores the challenges of supporting big data processing and cloud-based platforms as a proposed solution. Emphasizing a number of crucial topics such as data analytics, wireless networks, mobile clouds, and machine learning, this publication meets the research needs of data analysts, IT professionals, researchers, graduate students, and educators in the areas of data science, computer programming, and IT development. |
| cloud-based data management: Data-Driven Modelling and Predictive Analytics in Business and Finance Alex Khang, Rashmi Gujrati, Hayri Uygun, R. K. Tailor, Sanjaya Gaur, 2024-07-24 Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices. |
| cloud-based data management: Cloud Computing Service and Deployment Models: Layers and Management Bento, Al, 2012-10-31 This book presents a collection of diverse perspectives on cloud computing and its vital role in all components of organizations, improving the understanding of cloud computing and tackling related concerns such as change management, security, processing approaches, and much more--Provided by publisher. |
| cloud-based data management: Cloud Computing Venkata Josyula, Malcolm Orr, Greg Page, 2012 The complete guide to provisioning and managing cloud-based Infrastructure as a Service (IaaS) data center solutions Cloud computing will revolutionize the way IT resources are deployed, configured, and managed for years to come. Service providers and customers each stand to realize tremendous value from this paradigm shift--if they can take advantage of it. Cloud Computing brings together the realistic, start-to-finish guidance they need to plan, implement, and manage cloud solution architectures for tomorrow's virtualized data centers. It introduces cloud newcomers to essential concepts, and offers experienced operations professionals detailed guidance on delivering Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). This book's replicable solutions and fully-tested best practices will help enterprises, service providers, consultants, and Cisco partners meet the challenge of provisioning end-to-end cloud infrastructures. Drawing on extensive experience working with leading cloud vendors and integrators, the authors present detailed operations workflow examples, proven techniques for operating cloud-based network, compute, and storage infrastructure; a comprehensive management reference architecture; and a complete case study demonstrating rapid, lower-cost solutions design. Cloud Computing will be an indispensable resource for all network/IT professionals and managers involved with planning, implementing, or managing the next generation of cloud computing services. Venkata (Josh) Josyula, Ph.D., CCIE(R) No. 13518 is a Distinguished Services Engineer in Cisco Services Technology Group (CSTG) and advises Cisco customers on OSS/BSS architecture and solutions. Malcolm Orr, Solutions Architect for Cisco's Services Technology Solutions, advises telecoms and enterprise clients on architecting, building, and operating OSS/BSS and cloud management stacks. He is Cisco's lead architect for several Tier 1 public cloud projects. Greg Page has spent the last eleven years with Cisco in technical consulting roles relating to data center architecture/technology and service provider security. He is now exclusively focused on developing cloud/IaaS solutions with service providers and systems integrator partners. - Review the key concepts needed to successfully deploy clouds and cloud-based services - Transition common enterprise design patterns and use cases to the cloud - Master architectural principles and infrastructure designs for real-time managed IT services - Understand the Cisco approach to cloud-related technologies, systems, and services - Develop a cloud management architecture using ITIL, TMF, and ITU-TMN standards - Implement best practices for cloud service provisioning, activation, and management - Automate cloud infrastructure to simplify service delivery, monitoring, and assurance - Choose and implement the right billing/chargeback approaches for your business - Design and build IaaS services, from start to finish - Manage the unique capacity challenges associated with sporadic, real-time demand - Provide a consistent and optimal cloud user experience This book is part of the Networking Technology Series from Cisco Press(R), which offers networking professionals valuable information for constructing efficient networks, understanding new technologies, and building successful careers. Category: Cloud Computing Covers: Virtualized Data Centers |
| cloud-based data management: Artificial Intelligence and Internet of Things based Augmented Trends for Data Driven Systems Anshu Singla, Sarvesh Tanwar, Pao-Ann Hsiung, 2024-07-31 This book comprehensively discusses the role of cloud computing in artificial intelligence‐based data‐driven systems and hybrid cloud computing for large data‐driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‐based frameworks and advanced computing techniques to deal with online/virtual systems. This book: • Covers the aspects of security, authentication, and prediction for data‐driven systems in heterogeneous environments. • Provides data‐driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‐making for real‐time problems. • Showcases deep learning‐based computer vision algorithms for enhanced pattern detection in different domains based on data‐centric approaches. • Examines the role of the Internet of Things and machine learning algorithms for data‐driven systems. • Highlights the applications of data‐driven systems and cloud computing in enhancing network performance. This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering. |
| cloud-based data management: Applications of Big Data in Large- and Small-Scale Systems Goundar, Sam, Rayani, Praveen Kumar, 2021-01-15 With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems. |
| cloud-based data management: Agile Data Warehouse Design Lawrence Corr, Jim Stagnitto, 2011-11 Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. This book describes BEAM✲, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM✲ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: ✲ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM✲) ✲ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! ✲ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) ✲ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail ✲ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development ✲ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply ✲ Agile design documentation: enhancing star schemas with BEAM✲ dimensional shorthand notation ✲ Solving difficult DW/BI performance and usability problems with proven dimensional design patterns Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino. |
| cloud-based data management: Secure Data Management Willem Jonker, Milan Petković, 2014-05-14 This book constitutes the refereed proceedings of the 10th VLDB Workshop on Secure Data Management held in Trento, Italy, on August 30, 2013. The 15 revised full papers and one keynote paper presented were carefully reviewed and selected from various submissions. The papers are organized in technical papers and 10 vision papers which address key challenges in secure data management and indicate interesting research questions. |
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