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Journal articles on the topic 'Database Scalability'
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Sai Venkata Kondapalli. "Cloud Database Scalability: Meeting Modern Enterprise Demands." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2278–90. https://doi.org/10.30574/wjaets.2025.15.1.0469.
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Cloud database technologies have emerged as a critical solution for enterprises grappling with explosive data growth and unpredictable workload patterns. This comprehensive article examines how modern cloud database systems address enterprise scalability challenges through dynamic resource allocation, distributed architectures, and automated management capabilities. Further, we deep dive into the core scalability technologies, including horizontal and vertical scaling approaches, automatic scaling mechanisms, and distributed database architectures that enable organizations to handle exponentially growing datasets. The article further analyzes various database service models (DBaaS, cloud-native distributed databases, self-managed deployments), resource optimization strategies (connection pooling, query optimization, workload management), and crucial implementation considerations for successful cloud database migrations. Through real-world examples across industries, this article demonstrates how properly implementing these technologies allows enterprises to balance performance requirements with cost optimization while maintaining the business agility required in today's data-driven landscape.
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Pokorny, Jaroslav. "NoSQL databases: a step to database scalability in web environment." International Journal of Web Information Systems 9, no. 1 (2013): 69–82. http://dx.doi.org/10.1108/17440081311316398.
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Gupta Lakkimsetty, N. V. Rama Sai Chalapathi. "Database Optimization Strategies: Enhancing Performance and Scalability." International Journal of Computer Science and Mobile Computing 12, no. 11 (2023): 69–89. https://doi.org/10.47760/ijcsmc.2023.v12i11.006.
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Database optimization is critical for ensuring efficient data retrieval and storage, enabling high performance and scalability in modern applications. This paper explores comprehensive strategies for optimizing databases, focusing on query performance, schema design, caching, scalability, and cloud-based databases. Through an analysis of best practices and advanced techniques, this paper highlights methods to improve performance metrics and reduce bottlenecks, with a focus on practical implementation for real-world scenarios.
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Priyanka, Gowda Ashwath Narayana Gowda. "SQL vs. NoSQL Databases: Choosing the Right Option for FinTech." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 100–104. https://doi.org/10.5281/zenodo.13950855.
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The paper discusses the critical decision-making in choosing between SQL and NoSQL databases for FinTech applications. FinTech, founded on large-scale data processing, transactional integrity, and real-time analytics, warrants robust and highly scalable database solutions. SQL databases are very suitable for applications such as payment processing, customer relationship management, and core banking systems because of their strong consistency, reliability, and mature ecosystem. On the other hand, NoSQL databases offer flexibility in handling unstructured data, horizontal scalability, and high availability for big data analytics, real-time fraud detection, and personalized finance services. The paper contrasts SQL and NoSQL databases concerning data structure, scalability, consistency, and availability statements of strengths and limitations in FinTech. We provide insights into which database type would be more applicable for specific FinTech applications through several practical use cases and performance evaluations. The analysis describes that SQL databases are very relevant in cases with high transactional integrity within the application or system and structured data management. In contrast, a NoSQL database would find an application in scenarios requiring flexibility and scalability with diverse data types. FinTech companies, thereby, have to think very carefully about individual needs and options to choose the right database technology, ensuring it aligns with operational requirements and strategies for future growth.
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Naqvi, Kaynat Zehra. "Difficulties Associated with Replicated Data in Distributed Real-Time Database Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30824.
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In both Distributed and Real Time Databases Systems replication are interesting areas for the new researchers. In this paper, we provide an overview to compare replication techniques available for these database systems. Data consistency and scalability are the issues that are considered in this paper. Those issues are maintaining consistency between the actual state of the real-time object of the external environment and its images as reflected by all its replicas distributed over multiple nodes. We discuss a frame to create a replicated real- time database and preserve all timing constrains. In order to enlarge the idea for modelling a large scale database, we present a general outline that consider improving the Data consistency and scalability by using and accessible algorithm applied on the both database, with the goal to lower the degree of replication enables segments to have individual degrees of replication with the purpose of avoiding extreme resource usage, which all together contribute in solving the scalability problem for Distributed Real Time Database Systems. Keywords— Replicated database, Replicated Database Design, Replicated database protocols, Transactional replication, Data consistency and Scalability, Active and Passive replication.
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Guleria, Pratiyush. "Data Access Layer: A Programming Paradigm on Cloud." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (2013): 2341–45. http://dx.doi.org/10.24297/ijct.v11i3.1164.
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Database is important for any application and critical part of private and public cloud platforms. For compatibility with cloud computing we can follow architectures like three tier architecture in .Net Technologies such that database layer should be separate from user and business logic layers. There are some other issues like following ACID properties in databases, providing dynamic scalability by using Shared-disk Architecture and efficient multi-tenancy, elastic scalability, and database privacy.
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Swapnil, Raj, and Kumar Raghav Anuj. "Elasticity in the cloud related to database autonomies and scalability." i-manager’s Journal on Cloud Computing 9, no. 1 (2022): 26. http://dx.doi.org/10.26634/jcc.9.1.18719.
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Cloud computing has been a very popular paradigm for implementing online applications. Scalability, elasticity, cost of use, and large-scale economies are the main reasons for the effective and widespread acceptance of cloud computing. In this paper, we outline our work to inject the aforementioned "cloud capabilities" into a database system designed to support various applications deployed in the cloud: designing scalable databases using autonomies database and elasticity that enables lightweight resiliency using low-cost live database migrations and an intelligent and autonomous controller designed for system management without human intervention.
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Helal, Abdelsalam, and Judson Fortner. "Achieving scalability in highly contentious database systems." Information Sciences 89, no. 1-2 (1996): 39–61. http://dx.doi.org/10.1016/0020-0255(95)00223-5.
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Shekhar Mishra. "Building Scalable Cloud Databases with Database Reliability Engineering." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1322–33. https://doi.org/10.32628/cseit251112125.
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This comprehensive article explores the evolution and implementation of Database Reliability Engineering (DBRE) in cloud environments, focusing on the transformation from traditional database management to modern cloud-based solutions. The article examines key aspects of scalable database architectures, including elastic scalability, serverless solutions, and advanced scaling techniques. The article investigates various strategies for ensuring database reliability, performance optimization, and cost management while addressing challenges in data distribution and consistency maintenance. Through analysis of multiple cloud platforms and implementation approaches, the article demonstrates how organizations can effectively leverage automation, monitoring, and best practices to build robust, scalable database solutions that meet contemporary demands for performance and reliability.
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Y. Aldailamy, Ali, Abdullah Muhammed, Waidah Ismail, and Abduljalil Radman. "Comparative Study for Load Management of HBase and Cassandra Distributed Databases in Big Data." International Journal of Engineering & Technology 7, no. 4.31 (2018): 375–80. http://dx.doi.org/10.14419/ijet.v7i4.31.23715.
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The advancement in cloud computing, the increasing size of databases and the emergence of big data have made traditional data management system to be insufficient solution to store and manage such large-scale data. Therefore, there has been an emergence of new mechanisms for data storage that can handle large-scale data. NoSQL databases are used to store and manage large amount of data. They are intended to be open source, distributed and horizontally scalable in order to provide high performance. Scalability is one of the important features of such systems, it means that by increasing the number of nodes, more requests can be served per unit of time. Distribution and scalability are always companied with load management, which provides load balancing of work among multiple nodes. Load management efficiency varies from system to another according to the used load balancing technique. In this study, HBase and Cassandra load management with scalability will be evaluated as they are the most popular NoSQL databases modeled based on BigTable. In particular, this paper will compare and analyze the load management for the distributed performance of HBase and Cassandra using standard benchmark tool named Yahoo! Cloud Serving Benchmark (YCSB). The experiments will measure the performance of database operations with a different number of connections using different numbers of operations, database size, and processing nodes. The experimental results showed that HBase can provide better performance as the number of connections increase in the presence of horizontal scalability. Â
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Sonia Anurag Dubey. "Performance Analysis of NoSQL Databases for Healthcare Applications: A Benchmarking Study of MongoDB, Cassandra and Couchbase." Journal of Information Systems Engineering and Management 10, no. 25s (2025): 380–98. https://doi.org/10.52783/jisem.v10i25s.4030.
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Introduction: The burgeoning volume of healthcare data necessitates real-time processing capabilities, driving a surge in demand for scalable and efficient database solutions. Objectives: This paper presents a comprehensive performance evaluation of three prominent NoSQL databases—MongoDB, Cassandra, and Couchbase—tailored for healthcare applications. We benchmark these databases across key performance metrics, including read and write throughput, latency, scalability, and fault tolerance, utilizing a realistic healthcare dataset - Medical Information Mart for Intensive Care III (MIMIC-III). Our analysis aims to elucidate the distinct strengths and weaknesses of each database in handling healthcare data. Methods: Our analysis aims to elucidate the distinct strengths and weaknesses of each database in handling healthcare data. By contrasting the flexibility and user-friendliness of MongoDB with the extreme scalability of Cassandra and the high performance of Couchbase in distributed environments, this research empowers healthcare information technology professionals and database administrators to make informed decisions regarding NoSQL database selection. Results: These findings contribute to the effective management of healthcare data, facilitating improved health outcomes. Conclusions: This study analyzes performance demonstration in detail among MongoDB, Cassandra, and Couchbase, owing to the merits and demerits for healthcare applications. Real-time healthcare data processing is adequately assessed in terms of throughput, latency, scalability, and fault tolerance benchmarks for their appropriateness. The flexible features of MongoDB present multiple advantages, whereas, with respect to operations that require scalability at high performance, Cassandra is more usually chosen. This therefore should assist any healthcare IT professional in making the right decision for the selection of their NoSQL database when it comes to effective data management and enhanced outcomes for healthcare.
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Kuhlenkamp, Jörn, Markus Klems, and Oliver Röss. "Benchmarking scalability and elasticity of distributed database systems." Proceedings of the VLDB Endowment 7, no. 12 (2014): 1219–30. http://dx.doi.org/10.14778/2732977.2732995.
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Bagui, Sikha, and Loi Tang Nguyen. "Database Sharding." International Journal of Cloud Applications and Computing 5, no. 2 (2015): 36–52. http://dx.doi.org/10.4018/ijcac.2015040103.
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In this paper, the authors present an architecture and implementation of a distributed database system using sharding to provide high availability, fault-tolerance, and scalability of large databases in the cloud. Sharding, or horizontal partitioning, is used to disperse the data among the data nodes located on commodity servers for effective management of big data on the cloud.
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Bharat Kumar Dokka and Er Vikhyat Gupta. "Cloud Database Migration and Modernization." International Journal for Research Publication and Seminar 16, no. 1 (2025): 326–43. https://doi.org/10.36676/jrps.v16.i1.140.
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Cloud database migration and modernization are now a priority for organizations that aim to harness the flexibility, scalability, and cost savings of cloud technology. In spite of extensive research on the topic during 2015-2024, issues still exist in migrating legacy databases in an efficient manner, with security, performance optimization, and regulatory compliance being the major issues. The research has discussed various migration approaches such as lift-and-shift, re-platforming, and re-architecting each with a set of trade-offs. Although the cloud-native databases provide elasticity and scalability, integration with existing enterprise systems is complex. Use of artificial intelligence (AI) and machine learning (ML) methods in database tuning and migration has been promising as it automates most of the migration process, but the absence of end-to-end automation tools is still a major gap. Moreover, increased usage of multi-cloud and hybrid cloud strategies is causing new barriers in managing performance, vendor lock-in, and uniform database operations in diverse environments.
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Al-Hamami, Alaa Hussein, and RafalAdeeb Al-Khashab. "Providing Availability, Performance, and Scalability By Using Cloud Database." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 08 (2014): 1070–74. https://doi.org/10.5281/zenodo.14752171.
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With the development of the internet, new technical and concepts have attention to all users of the internet especially in the development of information technology, such as concept is cloud. Cloud computing includes different components, of which cloud database has become an important one. A cloud database is a distributed database that delivers computing as a service or in form of virtual machine image instead of a product via the internet; its advantage is that database can be accessed from anywhere and anytime. In this paper, we explain the cloud computing database issue in general, cloud database in especially, selection the important characteristics of cloud database, types of cloud deployment models and finally decide which primary methods to run a database on the cloud. Also, we focus on two subjects, the first one, we tried to determine which type of the deployment model is the best to deliver database services and discuss the reasons for this determination. The second, we explain how the user dealing with database in the cloud computing. Depending on the second, we explain the main challenges which are affecting the cloud performance, and suggest a method to handle these challenges.
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Researcher. "DATABASE DILEMMA: NAVIGATING THE SEA OF CHOICES TO FIND YOUR PERFECT FIT." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 743–52. https://doi.org/10.5281/zenodo.13376419.
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The rapid growth of data and the emergence of diverse database solutions have made database selection a critical aspect of software development. This article presents a structured methodology for choosing the right database based on an organization's requirements and use cases. The proposed approach consists of five key steps: defining project requirements, identifying core database features, evaluating available database options, prioritizing and eliminating candidates, and conducting proof of concepts or benchmarks. Organizations can navigate the complex landscape of database options by considering data volume, performance expectations, scalability, and deployment models. The article highlights the importance of aligning the selected database with the application's specific needs to ensure optimal performance and cost-effectiveness.
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., Siti Hawa Hasibuan, and Muhammad Irwan Padli Nasution . "A Comparative Study of Relasional and NoSQL database for Big Data Analytics." Jurnal Pendidikan, Sains Dan Teknologi 2, no. 3 (2023): 513–16. http://dx.doi.org/10.47233/jpst.v2i3.1039.
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The Database technology suite is rapidly evolving and consistently answering solutions user needs, one of which is the ability of the database to accommodate one piece of information quite a lot, the ability of the Database to accommodate data must be balanced together speed in loading the information needed by the user. In these fast-paced times today’s sophisticated information technology forces us to be able to serve data requests as well as process data quickly and efficiently, but if a system has accommodated too much a lot of data will certainly experience a time constraint in processing the data. The research method used is the type of library research (Library Research). The data obtained is sourced from the literature. NoSQL database discussion literature searched using keywords: SQL, NoSQL, databases from various publication databases. The results of the study are comparisons between relational databases and NoSQL involving factors
 Such as data models, scalability, data flexibility, and query and analytics capabilities. Databases Relational is more suitable for structured data and strict consistency, whereas database NoSQL is better suited for unstructured data and prioritizes scalability and flexibility.
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Dheerendra, Yaganti. "Scalable Data Management: A Comparative Study of SQL, NewSQL, NoSQL with .NET Framework." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 114–18. https://doi.org/10.5281/zenodo.15240969.
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Relational database management systems (RDBMS), historically dominant for data management, are increasingly challenged by newer database paradigms such as NewSQL and NoSQL. With growing data volumes and performance demands, especially in web applications, developers frequently seek databases offering scalability, flexibility, and performance. This study presents a comparative overview of traditional SQL databases, NewSQL, and NoSQL systems, highlighting their suitability based on specific application scenarios. Furthermore, it discusses integrating these database systems with the .NET framework, providing a practical approach to employing these diverse technologies in .NET applications.
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Ahmad, Khaleel, Mohammad Shoaib Alam, and Nur Izura Udzir. "Security of NoSQL Database Against Intruders." Recent Patents on Engineering 13, no. 1 (2019): 5–12. http://dx.doi.org/10.2174/1872212112666180731114714.
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Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.
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Matcha, Sasibhushana, and Dr Reeta Mishra. "Database Selection and Management: Choosing the Right Database (SQL vs. NoSQL) for Your Application." International Journal of Research in Humanities and Social Sciences 13, no. 3 (2025): 68–88. https://doi.org/10.63345/ijrhs.net.v13.i3.5.
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Database selection and management are critical factors in the design and optimization of software applications. Choosing the right database system, whether SQL or NoSQL, significantly impacts the performance, scalability, and maintainability of the application. SQL (Structured Query Language) databases, such as MySQL and PostgreSQL, are traditionally used in applications that require a well-defined schema, data integrity, and complex querying capabilities. These relational databases excel in handling structured data with predefined relationships, ensuring ACID (Atomicity, Consistency, Isolation, Durability) properties for transactional operations. On the other hand, NoSQL databases, including MongoDB, Cassandra, and Couchbase, provide more flexibility by allowing schema-less data structures, which are ideal for applications dealing with unstructured or semi-structured data, high write loads, and the need for horizontal scaling. NoSQL databases are often preferred in environments where fast, large-scale data processing is essential, such as in big data, real-time analytics, and distributed applications. This paper explores the key differences between SQL and NoSQL databases, emphasizing their advantages and limitations. The selection criteria for the appropriate database system depend on various factors, including the nature of the data, the application’s requirements for scalability, consistency, and fault tolerance, as well as long-term maintainability. By understanding the strengths and weaknesses of both database paradigms, developers can make informed decisions on which system best suits their application’s needs. This research aims to guide database management strategies for modern application development, ensuring optimal performance and efficiency.
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Akinola, Samuel. "Trends in Open Source RDBMS: Performance, Scalability and Security Insights." Journal of Research in Science and Engineering 6, no. 7 (2024): 22–28. http://dx.doi.org/10.53469/jrse.2024.06(07).05.
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The landscape of open-source relational database management systems (RDBMS) is evolving rapidly, driven by the growing demand for scalable, secure, and high-performance data solutions. This paper investigates key trends in open-source RDBMS databases, aiming to contribute valuable insights to the database research community. A comprehensive literature review reveals current developments in performance optimization, scalability, security, and integration with emerging technologies. The research methodology involves a systematic analysis of prominent open-source RDBMS projects, including PostgreSQL, MySQL, and MariaDB. Performance optimization strategies are explored, emphasizing advancements in query execution, indexing, and data storage techniques. The paper provides a detailed investigation into key trends in open-source RDBMS Database, offering valuable insights for the database research community. Security considerations are paramount, and the paper explores performance optimization strategies, focusing on advancements in query execution, indexing and data storage techniques. Furthermore, the integration of machine learning techniques for query optimization and predictive analytics is explored, highlighting the synergies between database management and artificial intelligence. The research emphasizes the evolving relationship between traditional RDBMS systems and newer paradigms such as NewSQL and NoSQL databases. It addresses the integration of flexible schema design and horizontal scalability into open-source RDBMS, fostering adaptability to diverse data models. The implications of these trends for the broader database community are discussed, paving the way for future research directions. As open-source RDBMS databases continue to play a pivotal role in data management, understanding and harnessing these trends is crucial for researchers, practitioners, and organizations seeking effective and future-proof data solutions. Furthermore, the paper explores performance optimization strategies, focusing on advancements in query execution, indexing, and data storage techniques It discusses compliance and regulatory considerations, shedding light on the evolving landscape of industry standards. The importance of incident response and disaster recovery planning within the context of cloud environments is also explored, offering insights into strategies for effective mitigation and recovery. The paper delves into emerging technologies and trends shaping the future of cloud computing security, with a focus on innovations like zero-trust security, edge computing, and AI-driven solutions. Real-world case studies underscore the practical application of security principles, providing tangible examples of successful implementations. The dynamic evolution of open-source relational database management systems (RDBMS) necessitates a thorough exploration of current trends and future trajectories. This paper meticulously examines performance optimization, scalability, security, and the intersection of RDBMS with cutting-edge technologies such as machine learning and cloud computing. Through a systematic analysis of prominent open-source RDBMS projects, this study elucidates the challenges and opportunities in these domains, offering a comprehensive roadmap for researchers, practitioners, and organizations dedicated to achieving excellence in data management. The rapidly evolving landscape of open-source relational database management systems RDBMS demands a comprehensive analysis of current trends and future directions. This paper delves into performance optimization, scalability, security, and the integration of RDBMS with emerging technologies like machine learning and cloud computing. By systematically analyzing prominent open-source RDBMS projects and investigating the challenges and opportunities in these areas, this study aims to provide a roadmap for researchers, practitioners, and organizations committed to data management excellence. In conclusion, this paper paints a comprehensive picture of the current state of cloud computing security while emphasizing the dynamic nature of the field. As organizations navigate an ever-evolving threat landscape, a continuous commitment to robust security measures and a forward-looking approach are crucial to realizing the full potential of cloud computing while safeguarding digital assets. Purpose: The purpose of this article is to explore and analyze the current trends, challenges, and future directions in the field of open-source RDBMS, emphasizing performance optimization, scalability, and security aspects. It aims to serve as a comprehensive resource for the database research community and professionals in the field. Significance: The significance of this article lies in its thorough analysis of open-source RDBMS trends and its contribution to understanding how these systems are evolving to meet modern data management challenges. It highlights the importance of performance, scalability, and security in the context of open-source RDBMS and their role in shaping the future of database technologies. Methods: The study employs a systematic approach, commencing with a comprehensive literature review of relevant sources. Subsequently, a detailed analysis of prominent open-source RDBMS projects ensues, focusing on critical aspects such as performance optimization strategies, scalability challenges, security enhancements, and integration with emerging technologies like machine learning and cloud computing. This methodological framework aims to provide a thorough understanding of the evolving landscape within the specified domains.
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Yijie Weng and Jianhao Wu. "Database management systems for artificial intelligence: Comparative analysis of postgre SQL and MongoDB." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2336–42. https://doi.org/10.30574/wjarr.2025.25.2.0586.
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The rapid evolution of artificial intelligence (AI) has amplified the need for efficient database management systems (DBMS) to handle the growing volume, variety, and velocity of data. PostgreSQL, a robust relational database, and MongoDB, a leading NoSQL solution, are two widely adopted DBMSs in AI applications, each offering unique advantages. This paper provides a comprehensive comparative analysis of PostgreSQL and MongoDB, focusing on their suitability for AI use cases. Key evaluation criteria include data modeling, query complexity, scalability, ACID compliance, indexing, and integration with AI frameworks. PostgreSQL excels in scenarios requiring strict data consistency, complex querying, and structured data, making it ideal for financial modeling, scientific research, and feature engineering. Conversely, MongoDB's schema-less design, horizontal scalability, and native support for semi-structured data align with real-time analytics, IoT, and evolving AI datasets. The study highlights that the choice between the two databases depends on specific project requirements and proposes hybrid approaches to leverage their complementary strengths. This analysis aims to guide AI practitioners in making informed database decisions to optimize performance, scalability, and flexibility in AI systems.
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Kunda, Douglas, and Hazael Phiri. "A Comparative Study of NoSQL and Relational Database." Zambia ICT Journal 1, no. 1 (2017): 1–4. http://dx.doi.org/10.33260/zictjournal.v1i1.8.
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Relational Database and NoSQL are competing types of database models. The former has been in existence since 1979 and the latter since the year 2000. The demands of modern applications especially in web 2.0, 3.0 and big data have made NoSQL a popular database of choice. Choosing an appropriate database model to use is an important decision that developers must make based on the features of a given database model. This paper compares the features of Relational Databases and NoSQL to establish which database is better at supporting demands of modern applications. The paper also brings out the challenges of NoSQL. Finally, the paper concludes by determining whether Relational Databases would completely be replaced by NoSQL database models. The findings revealed that, Relational Databases are based on ACID model which emphasizes better consistency, security and offers a standard query language. However, Relational Databases have poor scalability, weak performance, cost more, face availability challenges when supporting large number of users and handle limited volume of data. NoSQL, on the other hand is based on the BASE model, which emphasizes greater scalability and provides a flexible schema, offers better performance, mostly open source, cheap but, lacks a standard query language and does not provide adequate security mechanisms. Both databases will continue to exist alongside each other with none being better than the other. The choice of the database to use will depend on the nature of the application being developed. Each database type has its own challenges and strengths, with relational database lacking of support for unstructured data while NoSQL lacks standardization and has poor security. Modern applications in web 2.0, 3.0 and big data are well suited to use NoSQL but, there are still many applications that rely on Relational Databases.
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Mahesh, Kumar Goyal, and Chaturvedi Rahul. "The Role of NoSQL in Microservices Architecture: Enabling Scalability and Data Independence." European Journal of Advances in Engineering and Technology 9, no. 6 (2022): 87–95. https://doi.org/10.5281/zenodo.14770388.
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Microservices architecture has completely changed how software systems are architected and are being constructed and the advantages are enhanced agility, scalability, and resilience. In this paper, we study the critical role NoSQL databases play in driving microservices into the success they enjoy today, with respect to scalability and data independence. Unlike relational databases, NoSQL databases have different data models and are distributed which suit the principles of microservices and each service can pick the most suitable database for the specific data it needs. The purpose of this polyglot persistence approach, along with the sharding and replication inherent to NoSQL, allows companies to create highly flexible and high performing apps. We take a look at various types of NoSQL databases: key value stores, document databases, wide column stores and graph databases, looking at pros and cons from the microservices point of view. In addition, it discusses how the NoSQL databases solve problems such as data consistency, distributed transaction, and schema change in a distributed database system. The paper illustrates how NoSQL is utilized by organizations to achieve data independence and fault tolerance, and to optimize performance, through case studies and examples. The paper examines the operational complexities and the required skill set to manage a polyglot persistence environment and reaches a conclusion that, though complicated, strategic adoption of NoSQL databases are a key enabler for organizations who seek a return on implementing microservices architecture. The future promises more synergy and innovation in the form of more resilient, scalable, and data driven applications of the NoSQL and Microservices.
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Oko-Odion, Courage. "Forecasting Techniques in Predictive Analytics: Leveraging Database Management for Scalability and Real-Time Insights." International Journal of Research Publication and Reviews 5, no. 12 (2024): 1400–1414. https://doi.org/10.55248/gengpi.5.1224.3506.
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Researcher. "ORACLE 19C SHARDING: A COMPREHENSIVE GUIDE TO MODERN DATA DISTRIBUTION." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 637–47. https://doi.org/10.5281/zenodo.13880818.
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ABSTRACTOracle 19C Sharding is a new feature that represents a data management capability that empowers users to the arrangement of one database spread across a collection of databases (shards). They do not have hardware or software systems in place for this method. Enhancing scalability and availability while ensuring disaster recovery on a scale is crucial for mission critical applications by dividing data into smaller fragments which makes it easy to handle. Oracle 19c Sharding enhances reliability by minimizing the chance of bottlenecks and vulnerabilities in a system. Oracle Sharding supports global and local transactions which ensures data consistency and maintains integrity throughout the database across all the shards. The key features of Oracle 19c Sharding are automatic shard management, multi-shard queries and seamless integration to other high availability technologies like Data Guard, Golden- Gate and Real Application clusters. By implementing database sharding, organizations can get benefits of greater performance, flexibility and scalability from the database environment.
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Reshma, K.R, K.R Reshma, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, MAY (2016): 1–10. https://doi.org/10.5281/zenodo.3463026.
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ABSTRACT Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work aims to investigate and compare the performance and scalability of relational databases to graph databases in terms of handling multilevel queries such as finding the impact of a particular subject with the working area of pass out students. MySQL was chosen as the relational database, Neo4j as the graph database. KEYWORDS Neo4j, NOSQL, Graph database
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Zhou, Peng, Mei Li, Jing Huang, and Hua Fang. "Research on Database Schema Comparison of Relational Databases and Key-Value Stores." Advanced Materials Research 1049-1050 (October 2014): 1860–63. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1860.
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With the rapid development of Internet technology, the management capacity of traditional relational databases becomes relatively inefficient when facing the access and processing of big data. As a kind of non-relational databases, the key-value stores, with its high scalability, provide an efficient solution to the problem. This article introduces the concept and features of Key-Value stores, and followed by the comparison with the traditional relational databases, and an example is illustrated to explain its typical application and finally the existing problems of Key-Value stores are summarized.
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Laishevskiy, Ivan, Artem Barger, and Vladimir Gorgadze. "A Journey Towards the Most Efficient State Database For Hyperledger Fabric." Advances in Artificial Intelligence and Machine Learning 03, no. 04 (2023): 1526–56. http://dx.doi.org/10.54364/aaiml.2023.1188.
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Hyperledger Fabric is a leading permissioned blockchain platform known for its flexibility and customization. A crucial yet often overlooked component is its state database, which records the current state of blockchain applications. While the platform currently supports LevelDB and CouchDB, this study argues that there is an unmet need for exploring alternative databases to enhance performance and scalability. We evaluate RocksDB, Boltdb, and BadgerDB under various workloads, focusing on memory and CPU utilization. Our findings reveal that each alternative outperforms the existing options: RocksDB excels in throughput and latency, Boltdb minimizes CPU usage, and BadgerDB is most memory-efficient. This research not only provides a roadmap for integrating new state databases into Hyperledger Fabric but also offers critical insights for those aiming to optimize enterprise blockchain systems. The study underscores the significant gains in scalability and performance that can be achieved by reconsidering the choice of state database.
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Reshma, K.R, Femy P.F Mary, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, no. 2 (2016): 1–10. https://doi.org/10.5281/zenodo.3698858.
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<strong>ABSTRACT </strong> Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work aims to investigate and compare the performance and scalability of relational databases to graph databases in terms of handling multilevel queries such as finding the impact of a particular subject with the working area of pass out students. MySQL was chosen as the relational database, Neo4j as the graph database. <strong>KEYWORDS</strong> Neo4j, NOSQL, Graph database
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Owuondo, Joseph. "A Comprehensive Health Electronic Record System with MySQL RDMS, QGIS Database and Mongo DB." International Journal of Latest Technology in Engineering, Management & Applied Science XII, no. VIII (2023): 31–38. http://dx.doi.org/10.51583/ijltemas.2023.12904.
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The advancement in data storage systems and novel data types has made organizations to stop relying on the use of simple client/server I.T infrastructure and leverage more on multiple categories of database systems to keep heterogeneous data. The study exploits the benefits around deploying hybrid relational database management systems and NoSQL systems while developing better electronic health records (EHR) systems within health facilities alongside facility decision support system (FDSS). More particularly, GIS, MySQL and Mongo DB databases were integrated to enhance EHR systems alongside offering improve clinical decision support. The study adopted experimental design to develop the Electronic Health Records System using GIS, MySQL and Mongo DB software to create the database. Findings revealed that the atomicity, consistency, isolation and durability feature typical of relational database management systems guaranteed data security, integrity, ease of access and efficient transaction processing. Mongo database offered the system a more precise internal data structure and solid scalability along with simplified mapping of application objects to the underlying database design. The GIS database enabled a clear visualization of patients’ geographical locations, medical facilities, and the physical location of the physicians. Integrating these database systems within the health care arena was instrumental in compelling application systems to adhere to the HIPAA EHR standards without compromising performance and scalability.
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Sunil Yadav. "Cloud Database Optimization: Strategies for Performance, Scalability, and Cost-Efficiency." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2958–67. https://doi.org/10.32628/cseit25112738.
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Cloud database optimization encompasses technical strategies designed to enhance performance, ensure scalability, and control costs in modern cloud computing environments. The transition from traditional on-premises database management to cloud-based solutions presents organizations with significant advantages alongside complex optimization challenges. This article synthesizes findings from extensive implementations across various enterprise environments to quantify the impact of key optimization strategies. The article demonstrates that properly implemented query optimization techniques significantly reduce resource consumption and execution time, while advanced indexing strategies substantially decrease I/O operations and associated costs. Both horizontal and vertical database partitioning approaches provide dramatic performance improvements for large datasets, enabling consistent performance despite substantial growth in data volume. Elastic scaling capabilities allow organizations to perform optimally during workload fluctuations while avoiding unnecessary provisioning costs. Comprehensive monitoring combined with proactive alerting systems enables early detection of performance issues before they impact end-users, with automated maintenance procedures ensuring continued optimization. The collective implementation of these strategies yields substantial improvements in application responsiveness and user experience while simultaneously reducing operational expenditures, making cloud database optimization an essential discipline for organizations seeking to maximize the benefits of cloud computing.
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Almeida, Arthur Lorenzi, Vinícius Junqueira Schettino, Thiago Jesus Rodrigues Barbosa, Pedro Fernandes Freitas, Pedro Gabriel Silva Guimarães, and Wagner Arbex. "Relative Scalability of NoSQL Databases for Genotype Data Manipulation." Revista de Informática Teórica e Aplicada 25, no. 2 (2018): 93. http://dx.doi.org/10.22456/2175-2745.79334.
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Genotype data manipulation is one of the greatest challenges in bioinformatics and genomics mainly because of high dimensionality and unbalancing characteristics. These peculiarities explains why Relational Database Management Systems (RDBMSs), the "de facto" standard storage solution, have not been presented as the best tools for this kind of data. However, Big Data has been pushing the development of modern database systems that might be able to overcome RDBMSs deficiencies. In this context, we extended our previous works on the evaluation of relative performance among NoSQLs engines from different families, adapting the schema design in order to achieve better performance based on its conclusions, thus being able to store more SNP markers for each individual. Using Yahoo! Cloud Serving Benchmark (YCSB) benchmark framework, we assessed each database system over hypothetical SNP sequences. Results indicate that although Tarantool has the best overall throughput, MongoDB is less impacted by the increase of SNP markers per individual.
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Shin, David, Tony Sahama, Steve Jung-Tae Kim, and Ji-Hong Kim. "The Scalability and the Strategy for EMR Database Encryption Techniques." Journal of information and communication convergence engineering 9, no. 5 (2011): 577–82. http://dx.doi.org/10.6109/jicce.2011.9.5.577.
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Cao, Jian, and Cong Yan. "A Database Grid Service Based on CSGPA." Applied Mechanics and Materials 543-547 (March 2014): 3419–22. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3419.
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Database grid service provides users with a unified interface to access to distributed heterogeneous databases resources. To overcome the weakness of collaborative services ability in different grid portal, a new grid portal architecture based on CSGPA (Collaborative Services Grid Portal Architecture), is proposed. It devotes integrating database into Grid environment with grid service. In comparison with the current mainstream grid portal architecture, the results show that CSGPA has great advantage in efficiency, deployment costs, scalability and reusability etc.
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Fouad, Mohammed M., Mostafa G. M. Mostafa, Abdulfattah S. Mashat, and Tarek F. Gharib. "IMIDB: An Algorithm for Indexed Mining of Incremental Databases." Journal of Intelligent Systems 26, no. 1 (2017): 69–85. http://dx.doi.org/10.1515/jisys-2015-0107.
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AbstractAssociation rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size.
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Dzaki, Muhadzib. "Comparative Analysis of Performance of Relational and NoSQL Database Systems in E-Commerce Information Applications." Journal of Technology and Engineering 1, no. 2 (2023): 35–40. https://doi.org/10.59613/journaloftechnologyandengineering.v1i2.244.
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The rapid development of information technology has driven the need for a database system that is able to manage data efficiently, especially in e-commerce information applications that have large and dynamic data volumes. This study aims to analyze and compare the performance of relational database systems (RDBMS) with NoSQL databases in the context of e-commerce applications. The research is carried out with a qualitative approach through the literature study method or library research that examines various academic sources, including journals, books, and previous research reports. The analysis was carried out on several key performance aspects, such as scalability, speed of data access, flexibility of data structure, and ability to handle big data. The results show that relational databases still excel in the consistency of complex data and transactions, but have limitations in terms of horizontal scalability. On the other hand, NoSQL databases, such as MongoDB and Cassandra, show better performance in handling unstructured, large-scale data, and fast access needs, although at the expense of some aspects of consistency. These findings indicate that the selection of database types needs to be adjusted to the needs of the system and the characteristics of the data managed. This study makes a theoretical contribution to understanding the comparative performance of the two systems and becomes a reference for e-commerce system developers in determining the optimal database architecture.
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Keswani, Gunjan, and Manoj B. Chandak. "Enhancing database query interpretation: a comparative analysis of semantic parsing models." International Journal of Informatics and Communication Technology (IJ-ICT) 14, no. 2 (2025): 467. https://doi.org/10.11591/ijict.v14i2.pp467-477.
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The rapid proliferation of NoSQL databases in various domains necessitates effective parsing models for interpreting NoSQL queries, a fundamental aspect often overlooked in database management research. This paper addresses the critical need for a comprehensive understanding of existing semantic parsing models tailored for NoSQL query interpretation. We identify inherent issues in current models, such as limitations in precision, accuracy, and scalability, alongside challenges in deployment complexity and processing delays. This review is pivotal, shedding light on the intricacies and inefficiencies of existing systems, thereby guiding future advancements in NoSQL database querying. This methodical comparison of these models across key performance metrics-precision, accuracy, recall, delay, deployment complexity, and scalability-reveals significant disparities and areas for improvement. By evaluating these models against both individual and combined parameters, we identify the most effective methods currently available. The impact of this work is far-reaching, providing a foundational framework for developing more robust, efficient, and scalable parsing models. This, in turn, has the potential to revolutionize the way NoSQL databases are queried and managed, offering significant improvements in data retrieval and analysis. Through this paper, we aim to bridge the gap between theoretical model development and practical database management, paving the way for enhanced data processing capabilities in diverse NoSQL database applications.
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Nuriev, Marat, Rimma Zaripova, Alexey Sinicin, Andrey Chupaev, and Maksim Shkinderov. "Enhancing database performance through SQL optimization, parallel processing and GPU integration." BIO Web of Conferences 113 (2024): 04010. http://dx.doi.org/10.1051/bioconf/202411304010.
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This article delves into the cutting-edge methodologies revolutionizing database management systems (DBMS) through the lens of SQL query optimization, parallel processing, and the integration of graphics processing units (GPUs). As the digital world grapples with ever-increasing volumes of data, the efficiency, speed, and scalability of database systems have never been more critical. The first section of the article focuses on SQL query optimization, highlighting strategies to refine query performance and reduce resource consumption, thus enhancing application responsiveness and efficiency. The discourse then transitions to parallel processing in databases, an approach that leverages multiple processors or distributed systems to significantly boost data processing capabilities. This segment explores the advantages of parallelism in managing large datasets and complex operations, addressing the challenges and the impact on system scalability and fault tolerance. Furthermore, the article examines the innovative application of GPUs in database management, a development that offers profound speedups for analytical and machine learning tasks within DBMS. Despite the complexities and the initial investment required, the utilization of GPUs is portrayed as a game-changer in processing largescale data, thanks to their highly parallel architecture and computational prowess. Together, these advancements signify a transformative shift in database technologies, promising to address the challenges of modern data management with unprecedented efficiency and scalability. This article not only elucidates these sophisticated technologies but also provides a glimpse into the future of database systems, where optimization, parallel processing, and GPU integration play pivotal roles in navigating the data-driven demands of the contemporary digital landscape.
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Yamada, Hiroyuki, Toshihiro Suzuki, Yuji Ito, and Jun Nemoto. "ScalarDB: Universal Transaction Manager for Polystores." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3768–80. http://dx.doi.org/10.14778/3611540.3611563.
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This paper presents ScalarDB, a universal transaction manager that achieves distributed transactions across multiple disparate databases. ScalarDB provides a database-agnostic transaction manager on top of its database abstraction; thus, it achieves transactions spanning various databases without depending on the transactional capability of underlying databases. ScalarDB is based on several research works and extended to provide a strong correctness guarantee (i.e., strict serializability), further performance optimizations, and several critical mechanisms for productization. In this paper, we describe the design and implementation of ScalarDB. We also present evaluation results showing that ScalarDB achieves database-spanning transactions with reasonable performance and near-linear scalability without sacrificing correctness. Finally, we share some case studies and lessons learned while building and running ScalarDB.
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Pina, Eduardo, Filipe Sá, and Jorge Bernardino. "NewSQL Databases Assessment: CockroachDB, MariaDB Xpand, and VoltDB." Future Internet 15, no. 1 (2022): 10. http://dx.doi.org/10.3390/fi15010010.
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Background: Relational databases have been a prevalent technology for decades, using SQL (Structured Query Language) to manage data. However, the emergence of new technologies, such as the web and the cloud, has brought the requirement to handle more complex data. NewSQL is the latest technology that incorporates the ability to scale and ensures the availability of NoSQL (Not Only SQL) without losing the ACID properties (Atomicity, Consistency, Isolation, Durability) associated with relational databases. Methods: We evaluated CockroachDB, MariaDB Xpand, and VoltDB with OSSpal methodology and experimentally using the Star Schema Benchmark (SSB). The scalability and performance capabilities of each database were assessed. Results: Applying the OSSpal methodology, the results showed that MariaDB Xpand outperformed CockroachDB and VoltDB. On the other hand, we concluded that with Star Schema Benchmark, CockroachDB had better scalability, while VoltDB had a faster query execution time. Conclusions: CockroachDB and VoltDB are the best performing databases in terms of scalability and performance.
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Samra, Halima E., Alice S. Li, Ben Soh, and Mohammed A. AlZain. "Review of Contemporary Database Design and Implication for Big Data." International Journal of Smart Education and Urban Society 12, no. 4 (2021): 1–11. http://dx.doi.org/10.4018/ijseus.2021100101.
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In general, databases provide a single comprehensive view suitable for analysis and relevant information for a variety of organizational purposes. The intent of this paper is to review the contemporary database design in terms of data modelling, process modelling, relational databases, and data storage. The review indicates the contemporary relational database architecture provides numerous advantages such as high consistency and availability. However, it is not suitable for big data because its performance decreases as the data grows and faces scalability constraints as it is impossible to scale horizontally, and its vertical growth is limited. An implication here is that big data requires more than a relational database and the traditional SQL.
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Aftab, Zain, Waheed Iqbal, Khaled Mohamad Almustafa, Faisal Bukhari, and Muhammad Abdullah. "Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping." Scientific Programming 2020 (July 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8813350.
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Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.
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Osemwegie, Omoruyi, Kennedy Okokpujie, Nsikan Nkordeh, Charles Ndujiuba, Samuel John, and Uzairue Stanley. "Performance Benchmarking of Key-Value Store NoSQL Databases." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 5333. http://dx.doi.org/10.11591/ijece.v8i6.pp5333-5341.
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<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>
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Martha, Suresh. "Databases Design - The Backbone of Data Driven Business Intelligence Systems." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 748–55. http://dx.doi.org/10.22214/ijraset.2024.58898.
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Abstract: In the era of information abundance, businesses rely on effective data management to extract meaningful insights that drive strategic decision-making. This chapter explores the pivotal role of database design as the cornerstone of robust and effective data-driven Business Intelligence (BI) systems. The intricate interplay between database architecture, performance optimization, and scalability is dissected to unveil the critical factors influencing the design process. This chapter delves into the evolving landscape of database technologies, from traditional relational databases to cutting-edge NoSQL solutions, assessing their suitability for diverse business contexts.
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Sai, Sneha. "Database Integration: Connecting RPA to Data Sources for Enhanced Functionality." Journal of Advances in Developmental Research 15, no. 1 (2024): 1–6. https://doi.org/10.5281/zenodo.14851315.
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Robotic Process Automation (RPA) has revolutionized the way businesses operate by automating repetitive tasks and improving efficiency. However, the integration of databases with RPA systems unlocks the next level of functionality and scalability. This white paper explores how database integration enhances RPA capabilities, showcases industry use cases, and highlights the potential savings and improvements achievable through such integrations.
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Chen, Yuxing, Anqun Pan, Hailin Lei, et al. "TDSQL: Tencent Distributed Database System." Proceedings of the VLDB Endowment 17, no. 12 (2024): 3869–82. http://dx.doi.org/10.14778/3685800.3685812.
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Distributed databases have become indispensable in contemporary computing and data processing, owing to their pivotal role in ensuring high availability and scalability. They effectively cater to the requirements of data management and high-concurrency access. However, developing a distributed database system that is well-suited for diverse application scenarios, particularly for large-scale applications, presents several challenges. These challenges include ensuring data consistency and achieving high levels of performance. This paper presents TDSQL, a distributed database system that prioritizes core design principles of distributed systems, including high availability, strong consistency, and scalability. In particular, TDSQL has achieved high performance through over a decade of practical experience and optimization in various modules, such as the kernel, synchronous replication, and transaction processing, in large-scale application scenarios. By conducting the TPC-C benchmark test, TDSQL demonstrated outstanding performance, achieving a throughput of 814 million tpmC across 1650 database nodes, with a jitter rate of less than 0.2%. This jitter rate is an order of magnitude lower than the standard required, showcasing the system's stability and reliability. During the 8-hour TPC-C standard stress test, TDSQL successfully completed over 860 billion transactions and processed 40 trillion order details, with zero forced rollbacks and zero data inconsistency.
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Vikas, Prajapati. "Cloud-Based Database Management: Architecture, Security, challenges and solutions." Journal of Global Research in Electronics and Communications (JGREC) 1, no. 1 (2025): 07–13. https://doi.org/10.5281/zenodo.14934833.
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The rapid evolution of cloud computing has revolutionized database management, offering scalable, flexible, and cost-effective solutions for managing large volumes of data. Cloud computing has transformed database management by providing scalable, flexible, and cost-effective solutions for handling massive volumes of data. Cloud-Based Database Management Systems (CDBMS) eliminate the need for extensive on-premise infrastructure, enabling organizations to focus on core operations. These systems leverage advanced architectures, including layered designs that enhance scalability, interoperability, and fault tolerance, ensuring efficient data management. However, adopting cloud databases brings significant challenges, such as data security, multi-tenancy vulnerabilities, and achieving seamless scalability. This paper examines the architecture of CDBMS, highlighting its modular components and their role in maintaining data integrity, access control, and availability. Security challenges, including authentication, encryption, and misconfigurations, are analyzed alongside their implications for data privacy and operational resilience. Emerging solutions such as AI-driven database technologies, edge computing integration, and hybrid cloud strategies are explored to address these challenges. Additionally, the study evaluates the growing role of automation and orchestration tools in optimizing cloud operations by providing a comprehensive review of CDBMS architecture, security concerns, and innovative solutions.
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Bhatewara, Ankita, and Kalyani Waghmare. "Highly Scalable Network Management Solution Using Cassandra." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (2014): 5085–89. http://dx.doi.org/10.24297/ijct.v13i10.2330.
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With the current emphasis on Big Data, NOSQL databases have surged in popularity. These databases are claimed to perform better than SQL databases. The traditional database is designed for the structured data and the complex query. In the environment of the cloud, the scale of data is very large, the data is non-structured, the request of the data is dynamic, these characteristics raise new challenges for the data storage and administration, in this context, the NOSQL database comes into picture. This paper discusses about some non-structured databases. It also shows how Cassandra is used to improve the scalability of the network compared to RDBMS.
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Mr. Godly C Mathew Zachariah, Mr. Sachu Santhosh, Mr. Anandhrosh S, Mr. Shibin Thomas, and Cina Mathew. "Database and Modern Database Technology." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 12 (2024): 3680–86. https://doi.org/10.47392/irjaem.2024.0546.
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This paper articulates a holistic approach to study the future of database development as well as the interaction between AI and modern database technologies. Overall architecture is data-centric so that quality of data, security, and governance are enhanced. It addresses the scalability and cost-effectiveness of cloud-native versus serverless database solutions and introduces AI-powered approaches towards the management of databases like predictive maintenance, self-healing, and XAI toward transparency and accountability. Methodology: Real-time Data Analysis Real-time data analysis involving new tools such as Apache Kafka and Spark Streaming coupled with emerging AI techniques - GNNs, NLP, reinforcement learning, transfer learning, and quantum computing. Comparative analysis with Google Cloud AI Platform along with a comparison of its AI tools and platforms and even against another tool like Apache Cassandra that is used to implement such real-world applications, studying their efficiency in it. Finally, the research suggests strategies that will help in future-proofing database management with robust data governance, continuous learning, stakeholder collaboration, and adaptability to evolving technologies. This methodology is designed to draw on theoretical research, experimental validation, and practical case studies to provide a structured framework in which AI can be leveraged to drive innovation and sustainability in database systems.