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Cloud Database: Empowering Scalable and Flexible Data Management

Author: Dr. A. Karunamurthy

Publisher: Quing: International Journal of Innovative Research in Science and Engineering

Published: 2023-03-30

Total Pages: 23

ISBN-13:

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.

Data Management in the Cloud

Author: Divyakant Agrawal

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 120

ISBN-13: 3031018958

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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

Data Management in the Cloud

Author: Divyakant Agrawal

Publisher: Morgan & Claypool Publishers

Published: 2012

Total Pages: 141

ISBN-13: 1608459241

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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.

Fast and Scalable Cloud Data Management

Author: Felix Gessert

Publisher: Springer Nature

Published: 2020-05-15

Total Pages: 199

ISBN-13: 3030435067

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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.

Mastering Google Bigtable database

Author: Cybellium Ltd

Publisher: Cybellium Ltd

Published:

Total Pages: 173

ISBN-13:

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Harness the Power of Massive-Scale Data with "Mastering Google Bigtable Database" In the realm of modern data management, handling colossal datasets with precision and efficiency is paramount. "Mastering Google Bigtable Database" serves as your definitive guide to mastering one of the most powerful and scalable NoSQL databases – Google Bigtable. Whether you're an experienced data professional or a newcomer to the world of distributed databases, this book equips you with the knowledge and skills needed to harness the true capabilities of Google Bigtable. About the Book: "Mastering Google Bigtable Database" takes you on an enlightening journey through the intricacies of this dynamic distributed database. From foundational concepts to advanced techniques, you'll explore the depths of Google Bigtable's architecture, data model, and rich features. Each chapter is meticulously designed to provide both a deep understanding of the concepts and hands-on experience through practical applications. Key Features: · Foundations of Bigtable: Build a strong understanding of Google Bigtable's architecture, data model, and the underlying principles that make it a cornerstone of distributed data storage. · Data Modeling: Learn how to design efficient schema structures that optimize storage, retrieval, and performance in Google Bigtable. · Rich Data Manipulation: Master the art of manipulating data in Google Bigtable using its API, and explore advanced querying techniques for flexible data access. · Scalability and Performance: Dive into the realm of horizontal scaling and learn strategies to ensure smooth performance as your data grows exponentially. · Integration with Ecosystem: Explore how Google Bigtable seamlessly integrates with other Google Cloud services and popular tools, enabling you to build robust data pipelines and applications. · Advanced Topics: Uncover advanced concepts like data versioning, backup and recovery, security mechanisms, and best practices for high availability. · Real-World Use Cases: Gain insights from real-world examples across industries, from IoT and e-commerce to analytics and more, showcasing the versatility of Google Bigtable. · Administering Bigtable: Learn the essentials of managing Google Bigtable clusters, optimizing performance, and ensuring data security and compliance. Who This Book Is For: "Mastering Google Bigtable Database" caters to data engineers, database administrators, developers, and anyone eager to harness the potential of distributed databases. Whether you're seeking to enhance your skills or dive into the world of NoSQL databases, this book provides the insights and tools to navigate the complexities of Google Bigtable. Why You Should Read This Book: In an era of data explosion, Google Bigtable emerges as a powerful solution for managing vast amounts of data across distributed environments. "Mastering Google Bigtable Database" empowers you to fully unleash its capabilities, enabling you to build high-performance applications, handle massive workloads, and extract valuable insights from your data. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Designing Cloud Data Platforms

Author: Danil Zburivsky

Publisher: Simon and Schuster

Published: 2021-04-20

Total Pages: 334

ISBN-13: 1617296449

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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.

Mastering NoSQL

Author: Cybellium Ltd

Publisher: Cybellium Ltd

Published:

Total Pages: 217

ISBN-13:

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Unleash the Potential of Flexible Data Storage In the dynamic landscape of modern data management, traditional relational databases often fall short in accommodating the diverse and ever-changing data needs. "Mastering NoSQL" is your comprehensive guide to understanding and harnessing the capabilities of NoSQL databases—a revolutionary approach to data storage that offers flexibility, scalability, and agility like never before. About the Book: The exponential growth of data, coupled with the rise of dynamic applications, has brought NoSQL databases to the forefront of data management. "Mastering NoSQL" provides a deep exploration of this paradigm, catering to both beginners and experienced professionals seeking to revolutionize the way they store, retrieve, and analyze data. Key Features: NoSQL Fundamentals: Begin your journey with an introduction to the foundational concepts of NoSQL. Understand the principles that set NoSQL apart from traditional relational databases. Diverse NoSQL Models: Delve into the various NoSQL database models, such as document stores, key-value stores, column-family stores, and graph databases. Learn the strengths and best use cases for each model. Scalability and Flexibility: Explore the scalability advantages offered by NoSQL databases. Understand how these databases accommodate the challenges of massive data growth and fluctuating workloads. Data Modeling: Grasp the unique data modeling approaches of NoSQL databases. Learn how to design schemas that adapt to evolving data requirements. Consistency and Availability: Understand the trade-offs between consistency and availability in NoSQL systems. Explore the CAP theorem and strategies for maintaining data integrity in distributed environments. Real-World Use Cases: Gain insights into how diverse industries leverage NoSQL databases to solve complex problems. From e-commerce to social networks, explore the applications that harness NoSQL's power. Migration Strategies: Discover techniques for migrating from traditional databases to NoSQL. Learn about data transformation, schema evolution, and ensuring a smooth transition. In a data-driven world, the need for flexible and scalable data storage solutions is paramount. "Mastering NoSQL" empowers database administrators, developers, and technology enthusiasts to unlock the potential of NoSQL databases, enabling them to build applications that thrive in the face of dynamic data demands. Embrace the Future of Data Storage: As the data landscape continues to evolve, NoSQL databases have emerged as a game-changing solution. "Mastering NoSQL" equips you with the knowledge needed to navigate this paradigm shift, allowing you to build resilient, adaptable, and scalable systems that thrive in the era of big data. Your journey to mastering the art of NoSQL begins here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Building Cloud Data Platforms Solutions

Author: Anouar BEN ZAHRA

Publisher: Anouar BEN ZAHRA

Published:

Total Pages: 339

ISBN-13:

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"Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud" comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.

CLOUD COMPUTING

Author: PRABHU TL

Publisher: NestFame Creations Pvt Ltd.

Published:

Total Pages: 166

ISBN-13:

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More and more, technology is moving to the cloud. It’s not just a fad — the shift away from traditional software models to software as a service, or Saas, has steadily gained momentum over the last 10 years. Looking ahead, the next decade of cloud computing promises even more ways to collaborate from anywhere, using mobile devices. So what is cloud computing? Essentially, cloud computing is a kind of outsourcing of software, data storage, and processing. Users access applications and files by logging in from any device that has an internet connection. Information and programs are hosted by outside parties and reside on a global network of secure data centers instead of on the user’s hard drive. This frees up processing power, facilitates sharing and collaboration, and allows secure mobile access regardless of where the user is or what device is being used. Cloud computing is a more efficient way of delivering computing resources. With cloud computing, software and service environments are subscription-based — users pay a monthly fee instead of buying licenses. Software and platforms are managed by the providers and are updated continuously for maximum performance and security. Computing power is remote instead of centralized, so users can tap into extra capacity if business spikes. Multiple people can access a shared program or file and collaborate in real time from different locations.

On the Security of NoSQL Cloud Database Services

Author: Mohammad Ahmadian

Publisher:

Published: 2017

Total Pages: 110

ISBN-13:

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Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a public cloud infrastructure where, the Cloud Service Provider (CSP) is not completely trusted by the data owner. We present a high level description of several architectures combining modern cryptographic primitives for achieving this goal. A novel searchable security scheme is proposed to leverage secure query processing in presence of a malicious cloud insider without disclosing sensitive information. A holistic database security scheme comprised of data confidentiality and information leakage prevention is proposed in this dissertation. The main contributions of our work are: (i) A searchable security scheme for non-relational databases of the cloud DBaaS; (ii) Leakage minimization in the untrusted cloud. The analysis of experiments that employ a set of established cryptographic techniques to protect databases and minimize information leakage, proves that the performance of the proposed solution is bounded by communication cost rather than by the cryptographic computational effort.