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Journal articles on the topic 'Scalable Data Architecture'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Bagam, Naveen. "Implementing Scalable Data Architecture for Financial Institutions." Stallion Journal for Multidisciplinary Associated Research Studies 2, no. 3 (2023): 27–40. https://doi.org/10.55544/sjmars.2.3.5.

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The finance sector generates vast volumes of complex data, which require scalable and robust architectures for efficient storage, processing, and analytics. Scalable data architecture is the basis that will make financial institutions competitive, compliant, and innovative in the modern fast-developing digital landscape. This paper addresses the principles, technologies, and methodologies necessary to implement scalable data architecture, keeping in mind high availability, security, and performance optimization as challenges. This paper is geared with real-world examples, technical frameworks,

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Venkata Surendra Reddy Appalapuram. "Hybrid data processing architectures: Balancing latency, complexity, and resource utilization in modern data ecosystems." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1832–41. https://doi.org/10.30574/wjaets.2025.15.2.0750.

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In order to meet the changing needs of contemporary data ecosystems, this article provides a thorough analysis of hybrid data processing architectures that blend batch and streaming paradigms. The content systematically analyzes three prominent architectural patterns: Separate Pipelines with Unified Storage, Lambda Architecture, and Kappa Architecture. Through detailed technical implementation considerations and real-world case studies spanning e-commerce, financial services, and IoT domains, the discussion evaluates how these architectures balance the competing demands of latency, complexity,

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Bharat Kumar Reddy Kallem. "Building a scalable enterprise data architecture for financial institutions." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1153–57. https://doi.org/10.30574/wjaets.2025.15.1.0249.

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Enterprise data architecture for financial institutions has evolved dramatically to address the exponential growth of financial data, which now exceeds 2.5 exabytes daily with a 40% annual growth rate. Traditional infrastructures struggle to meet modern operational demands, with a significant majority of institutions reporting scaling challenges. The shift toward real-time processing requirements compounds these difficulties as banking systems process billions of transactions daily while investment platforms handle hundreds of thousands of market data messages per second during volatility even

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Ram Rajendiran, Gautham. "Scalable Data Platform Architecture for Highly Variable e-Commerce Workloads." International Journal of Science and Research (IJSR) 9, no. 5 (2020): 1895–902. http://dx.doi.org/10.21275/sr24923122459.

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Urkudkar, Chetan. "Building Scalable ETL Pipelines for HR Data." American Journal of Engineering and Technology 07, no. 06 (2025): 88–95. https://doi.org/10.37547/tajet/volume07issue06-09.

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The article is devoted to the development and experimental validation of scalable ETL pipelines for HR data, aimed at bridging the gap between the volume of heterogeneous workforce events and the capabilities of traditional nightly processes. The relevance of the study is determined by the exponential growth of the HR technology market to USD 40.45 billion in 2024 and its forecasted doubling by 2032 at a 9.2% CAGR, as well as by the fragmentation of corporate systems, which leads to data incompleteness, inconsistency, and latency in turnover metrics and talent-development program effectiveness

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Venkata, Gummadi. "Designing a Scalable Architecture for Customer Data Engineering Platform on Cloud Infrastructure: A Comprehensive Framework." Journal of Scientific and Engineering Research 10, no. 12 (2023): 243–51. https://doi.org/10.5281/zenodo.14012383.

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The exponential growth of customer data in modern enterprises has created unprecedented challenges in data engineering, necessitating architectures capable of handling petabyte-scale processing while maintaining real-time analytics capabilities. This paper presents a comprehensive architectural framework for designing and implementing scalable customer data engineering platforms utilizing cloud infrastructure. The proposed architecture addresses critical challenges including real-time data processing, horizontal scalability, data governance, and security considerations. Through rigorous experi

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Singh, Mantu. "Implementing Service Mesh Architecture for Scalable Applications." American Journal of Engineering and Technology 7, no. 4 (2025): 157–65. https://doi.org/10.37547/tajet/volume07issue04-21.

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This study examines a decentralized approach to implementing a service mesh for microservice-based systems designed for scalable data processing. Unlike traditional solutions dominated by the pipes-and-filters pattern and a centralized control plane, this approach utilizes the concept of Eblocks—unified modules that incorporate service discovery, authentication, monitoring, and load management components. This allows for the formation of various patterns (manager-worker, divide-and-conquer, hybrid models) directly at the microservice level without strict dependence on centralized logic. It is

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Sumit Kumar Agrawal and Dr T. Aswini. "Multi-Tenant Low Latency Scalable Architectures for Large-Scale Customer Data Processing." International Journal for Research Publication and Seminar 16, no. 1 (2025): 174–94. https://doi.org/10.36676/jrps.v16.i1.41.

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In the era of big data, organizations are increasingly managing large volumes of customer data that need to be processed efficiently and scalably. Multi-tenant architectures provide an effective solution for such demands, especially when managing and processing data from multiple clients on a shared infrastructure. This paper explores the design and implementation of low-latency, scalable, multi-tenant architectures for large-scale customer data processing. By leveraging serverless computing, containerization, and distributed computing models, the architecture can dynamically scale according t

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Al-Fares, Mohammad, Alexander Loukissas, and Amin Vahdat. "A scalable, commodity data center network architecture." ACM SIGCOMM Computer Communication Review 38, no. 4 (2008): 63–74. http://dx.doi.org/10.1145/1402946.1402967.

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Cheruku, Saketh Reddy, Shalu Jain, and Anshika Aggarwal. "Building Scalable Data Warehouses: Best Practices and Case Studies." Darpan International Research Analysis 12, no. 1 (2024): 80–99. http://dx.doi.org/10.36676/dira.v12.i1.87.

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In today's data-driven world, the ability to manage, store, and analyze large volumes of data is crucial for business success. The demand for scalable data warehouses has risen dramatically as organizations seek to handle the explosion of data generated by modern applications and digital transactions. "Building Scalable Data Warehouses: Best Practices and Case Studies" explores the key strategies, methodologies, and technologies involved in designing and implementing scalable data warehouses that meet the demands of today and the future. The paper highlights the importance of architecture choi

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Pradeep Kumar Vattumilli. "Metadata-Driven ETL Pipelines: A Framework for Scalable Data Integration Architecture." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1799–807. https://doi.org/10.32628/cseit241061224.

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This article comprehensively analyzes metadata-driven data pipelines in Extract, Transform, and Load (ETL) processes, examining their architectural patterns, implementation strategies, and business impact. The article explores how metadata-driven approaches enhance pipeline flexibility, maintainability, and scalability compared to traditional ETL implementations. The article investigates the theoretical foundations of metadata-driven architectures and presents a framework for implementing reusable pipeline components through metadata templates. The article evaluates performance characteristics

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Akash Vijayrao Chaudhari and Pallavi Ashokrao Charate. "Optimizing Data Lakehouse Architectures for Scalable Real-Time Analytics." International Journal of Scientific Research in Science, Engineering and Technology 12, no. 2 (2025): 809–22. https://doi.org/10.32628/ijsrset25122198.

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Real-time analytics at scale demands data architectures that can ingest, process, and query large volumes of fast-moving data with low latency and strong consistency guarantees. The data lakehouse architecture has emerged as a promising paradigm, combining the schema enforcement, ACID transactions, and performance optimizations of data warehouses with the flexibility and scalability of data lakes. This paper provides a comprehensive overview of approaches to optimize data lakehouse architectures for scalable real-time analytics. We review the theoretical foundations of lakehouse systems and mo

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BELOKI, ZUHAITZ, XABIER ARTOLA, and AITOR SOROA. "A scalable architecture for data-intensive natural language processing." Natural Language Engineering 23, no. 5 (2017): 709–31. http://dx.doi.org/10.1017/s1351324917000092.

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AbstractComputational power needs have greatly increased during the last years, and this is also the case in the Natural Language Processing (NLP) area, where thousands of documents must be processed, i.e., linguistically analyzed, in a reasonable time frame. These computing needs have implied a radical change in the computing architectures and big-scale text processing techniques used in NLP. In this paper, we present a scalable architecture for distributed language processing. The architecture uses Storm to combine diverse NLP modules into a processing chain, which carries out the linguistic

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Natarajan, Loganandh. "Optimizing Cloud Architecture for Scalable Data Analytics and Advanced Data Science Capabilities." International Journal of Engineering and Computer Science 13, no. 12 (2024): 26677–97. https://doi.org/10.18535/ijecs/v13i12.4954.

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The relatively short timeframe of the data-oriented approach has made cloud architecture the basis for flexible and effective data analysis and data science projects. This paper presents the design strategies and considerations of cloud architectures for data science platforms that compliments modern analytics and machine learning workloads. Sub-processes like data acquisition, management, analysis, and coordination are discussed, as well as their part in supporting moment and science driven decision-making. Responsiveness is given on the use of tools and platforms that are built natively on c

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Saketh Reddy Cheruku, Shalu Jain, and Anshika Aggarwal. "Building Scalable Data Warehouses: Best Practices and Case Studies." Modern Dynamics: Mathematical Progressions 1, no. 2 (2024): 116–30. http://dx.doi.org/10.36676/mdmp.v1.i2.15.

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In today's data-driven world, the ability to manage, store, and analyze large volumes of data is crucial for business success. The demand for scalable data warehouses has risen dramatically as organizations seek to handle the explosion of data generated by modern applications and digital transactions. "Building Scalable Data Warehouses: Best Practices and Case Studies" explores the key strategies, methodologies, and technologies involved in designing and implementing scalable data warehouses that meet the demands of today and the future. The paper highlights the importance of architecture choi

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Ratna Vineel Prem Kumar Bodapati. "Data Mesh Architecture for Scalable Business Intelligence Systems." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 770–77. https://doi.org/10.32996/jcsts.2025.7.5.86.

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Data mesh architecture represents a transformative paradigm shift for scalable business intelligence systems, addressing fundamental limitations of traditional centralized approaches. By decentralizing data ownership around business domains, treating data as products with defined interfaces, establishing self-service infrastructure, and implementing federated governance, organizations can overcome bottlenecks that impede analytical agility. The architecture enables cross-functional teams to collaborate effectively while maintaining enterprise-wide consistency, resulting in accelerated insights

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Pattnaik, Ripunjaya. "DATA MESH: A MODERN APPROACH TO SCALABLE CLOUD DATA ARCHITECTURE." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY 16, no. 1 (2025): 1645–58. https://doi.org/10.34218/ijcet_16_01_121.

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Chinmay Mukeshbhai Gangani. "Developing Scalable Java Microservices for Healthcare Applications." Kuwait Journal of Advanced Computer Technology 1, no. 1 (2025): 18–28. https://doi.org/10.52783/kjact.269.

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Elderly people with infectious infections are challenging to treat; as they often present to consultations with severe, advanced symptoms, they are frequently sent to emergency care. The hypothesis was that a patient's health might be considerably enhanced and the strain on emergency health system services could be lessened by delaying an infectious illness diagnosis by a few days. Chatbots that can monitor a patient's status, deliver targeted information, promote drug adherence, and more might be especially helpful for patients with comorbidities or chronic illnesses. Chatbots need an appropr

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Dr. Pradeep Laxkar and Dr. Nilesh Jain. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2907–16. https://doi.org/10.32628/cseit25112764.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud

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Journal, of Global Research in Electronics and Communications. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." Journal of Global Research in Electronics and Communications 1, no. 4 (2025): 7–11. https://doi.org/10.5281/zenodo.15115138.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud

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Santosh, Vinnakota. "Optimizing Data Lakehouse Architectures for Large-Scale Analytics." Journal of Advances in Developmental Research 14, no. 1 (2023): 1–8. https://doi.org/10.5281/zenodo.15104183.

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The evolution of data architectures has led to the emergence of the data lakehouse, a hybrid model that combines the scalability of data lakes with the performance and governance capabilities of data warehouses. This paper explores best practices for implementing and maintaining a scalable, efficient lakehouse architecture, addressing key design considerations such as storage formats, metadata management, and query optimization.

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Seshendranath Balla Venkata. "Architecting Enterprise-Scale Data Products: A Framework for Advanced Data Science and AI/ML Operations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1724–34. https://doi.org/10.32628/cseit241061218.

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This article presents a comprehensive framework for building enterprise-scale data products that power modern Customer & Product Analytics, Data Science, artificial intelligence, and machine learning initiatives. The article examines the foundational architecture patterns, pipeline engineering strategies, and advanced distributed computing approaches in both on-prem and cloud. These are essential for developing robust data infrastructure capable of handling complex Data Analytics, Data Science, and AI/ML workflows. The article explores critical aspects of feature engineering at scale, real

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Rameshreddy Katkuri. "Revolutionizing healthcare with secure and scalable microservices." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1731–39. https://doi.org/10.30574/wjarr.2025.26.1.1235.

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This article explores how healthcare organizations are transforming patient care delivery through microservices architecture. By decomposing monolithic systems into modular components, healthcare institutions achieve greater flexibility, security, and scalability. The transition to microservices architecture enables more efficient electronic health record systems, responsive telemedicine platforms, and sophisticated real-time analytics capabilities. Healthcare providers implementing microservices experience improved system interoperability, reduced operational costs, and enhanced compliance ma

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Kulau, Ulf, Juergen Herpel, Ran Qedar, et al. "Towards modular and scalable on-board computer architecture." it - Information Technology 63, no. 4 (2021): 185–97. http://dx.doi.org/10.1515/itit-2020-0037.

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Abstract The demand for satellites and space systems with ever-increasing avionics requirements is constantly growing, whether in the field of satellite communications or earth observation. Traditional architectures for Data Handling Systems (DHS) on satellites are reaching their limits in terms of flexibility, interoperability and reusability, while slowing down the innovation cycle due to costly qualification. With regard to commercial and industrial solutions, it is evident that ‘plug and play’-like systems based on open standards can overcome the above-mentioned disadvantages. For this rea

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Researcher. "ENHANCING IOT SYSTEMS WITH SCALABLE CLOUD ARCHITECTURES FOR REAL-TIME DATA PROCESSING." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 774–86. https://doi.org/10.5281/zenodo.14229605.

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The development and application of Internet of Things (IoT) systems coupled with scalable cloud architectures for real-time data processing are examined in detail in this extensive essay. From data ingestion to security concerns, the paper explores the basic difficulties enterprises face while overseeing extensive IoT deployments. It explores the elements of cloud-based architecture, highlighting the crucial roles played by processing frameworks, storage options, and data intake levels. The essay discusses edge analytics integration and emphasizes how it can improve privacy, optimize bandwidth

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Singu, Santosh Kumar. "Leveraging Snowflake for Scalable Financial Data Warehousing." International Journal of Computing and Engineering 6, no. 5 (2024): 41–51. http://dx.doi.org/10.47941/ijce.2296.

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Purpose: The study discusses the increasing challenges faced by financial services due to fast-growing transaction, regulatory, and client data, and the need for more flexible, scalable, and affordable data management systems. It examines the potential of Snowflake, a cloud-based data warehousing platform, to address these issues through its multi-cluster shared data architecture Methodology: The paper analyzes Snowflake's architecture, focusing on its ability to decouple storage from compute, allowing organizations to scale resources as needed. Case studies of financial institutions implement

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Tarun, Parmar. "Scalable Data Architecture for Modern Manufacturing: Integrating Data Lakes and Pipelines." International Journal of Leading Research Publication 4, no. 2 (2023): 1–7. https://doi.org/10.5281/zenodo.14840171.

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The implementation of data lakes and data pipelines for scalable manufacturing analytics presents significant opportunities and challenges for modern manufacturing organizations. These technologies enable real-time monitoring, predictive maintenance, and optimization of production processes, leading to improved operational efficiency and strategic decision making. However, successful implementation requires addressing several key challenges, including data quality and consistency, the integration of legacy systems, real-time data processing, data security and compliance, scalability and perfor

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Shinghal, Deepti, Kshitij Shinghal, Amit Saxena, Shuchita Saxena, and Rajul Misra. "The Big Data for WSN Nodes: Leveraging Scalable Architecture." ITM Web of Conferences 57 (2023): 02006. http://dx.doi.org/10.1051/itmconf/20235702006.

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Certain applications requires a scalable cost effective storage and execution system with facility to store data and have feature to analyze data to its finest granularity level in future. This increase the quality and accuracy of result analysis. Wireless sensor Network (WSN) nodes deployed for certain data intensive applications such as surveillance, war zone monitoring etc. generates a massive amount of raw data. There is an essential requirement of storing this data in its native format for analytics purpose in anticipation of future requirements. In present work, a data lake implemented o

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Researcher. "SCALABLE AI-DRIVEN MICROSERVICES ARCHITECTURES FOR DISTRIBUTED CLOUD ENVIRONMENTS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 154–68. https://doi.org/10.5281/zenodo.14053729.

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This article presents a comprehensive approach to designing scalable AI-driven microservices architectures for distributed cloud environments. It explores key challenges in integrating AI into distributed systems and proposes strategies for microservices design, deployment, and scaling of AI workloads. The article covers data pipeline optimization, security, and compliance considerations and presents a detailed case study of a scalable image recognition service. Through analysis of scalability, efficiency, and robustness, the proposed architecture demonstrates significant improvements over tra

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Nikolic, Lazar, Vladimir Dimitrieski, and Milan Celikovic. "An approach for supporting transparent acid transactions over heterogeneous data stores in microservice architectures." Computer Science and Information Systems, no. 00 (2024): 6. http://dx.doi.org/10.2298/csis221210006n.

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Microservice architectures (MSA) are becoming a preferred architectural style for data-driven applications. A transaction within MSA can include remote calls to multiple services, turning it into a distributed transaction. Participating services may have their own data stores running local transactions with varying levels of transactional support and consistency guarantees. Coordinating distributed transactions in such an environment is a key challenge for MSA. The existing approaches are either highly consistent at the expense of scalability or scalable at the expense of consistency. Furtherm

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Nagaraj M, Raghavendra M Y, and Ameena Firdous Nikhat. "Scalable and secure network architectures for next-generation data centers." World Journal of Advanced Research and Reviews 10, no. 1 (2021): 397–406. http://dx.doi.org/10.30574/wjarr.2021.10.1.0114.

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As demand for high-performance, efficient, and secure data center operations rises, traditional network architectures are increasingly inadequate for modern digital ecosystems. Emerging technologies such as cloud computing, AI, IoT, and big data have overwhelmed existing infrastructures, driving the need for innovative solutions. This paper examines advancements in scalable frameworks, specifically Software-Defined Networking (SDN) and Network Function Virtualization (NFV). SDN centralizes control for dynamic traffic management, while NFV virtualizes network services to enhance flexibility and

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Shinu Pushpan. "Multi-Tenant Architecture: A Comprehensive Framework for Building Scalable SaaS Applications." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1117–26. https://doi.org/10.32628/cseit241061151.

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Multi-tenant architecture has emerged as a fundamental paradigm in modern software development, particularly in Software as a Service (SaaS) applications where multiple organizations share computing resources while maintaining data isolation. This article presents a comprehensive framework for understanding and implementing multi-tenant systems, focusing on essential architectural decisions and design patterns that ensure scalability, security, and resource efficiency. The article examines the evolution from single-tenant to multi-tenant architectures, analyzes various data partitioning strate

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Theofilou, Asterios, Stefanos A. Nastis, Michail Tsagris, Santiago Rodriguez-Perez, and Konstadinos Mattas. "Design and Implementation of a Scalable Data Warehouse for Agricultural Big Data." Sustainability 17, no. 8 (2025): 3727. https://doi.org/10.3390/su17083727.

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The rapid growth of agricultural data necessitates the development of storage systems that are scalable and efficient in storing, retrieving and analyzing very large datasets. The traditional relational database management systems (RDBMSs) struggle to keep up with large-scale analytical queries due to the volume and complexity inherent in those data. This study presents the design and implementation of a scalable data warehouse (DWH) system for agricultural big data. The proposed solution efficiently integrates data and optimizes data ingestion, transformation, and query performance, leveragin

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Isaev, Movladi I., Muhammed K. Tlastankulov, and Rayana A. M. Aibueva. "FUNCTIONAL PROGRAMMING IN SCALABLE MICROSERVICE ARCHITECTURES." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 12/9, no. 153 (2024): 162–68. https://doi.org/10.36871/ek.up.p.r.2024.12.09.018.

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The article discusses the use of functional programming (FP) for developing scalable microservice architectures. The main attention is paid to the key principles of FP: pure functions, data immutability, and higher-order functions. Their advantages for building fault-tolerant and predictable microservice systems are analyzed. The architecture of microservices using asynchronous data processing and reactive streams is described. Performance testing has shown an increase in throughput and a decrease in response time. Practical approaches to working with databases and asynchronous interaction bet

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J L, Amarnath. "Intellisearch - Intelligent Employee Data Access System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40772.

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- The IntelliSearch Intelligent Employee Data Access System leverages advanced machine learning (ML) and natural language processing (NLP) techniques to modernize employee information retrieval. Designed to address inefficiencies in traditional manual methods, IntelliSearch provides secure, real-time responses to queries about roles, attendance, salaries, and performance metrics. Its modular architecture integrates with existing databases, offering robust data security through encryption and role-based access control (RBAC). By streamlining repetitive tasks and enhancing query accuracy, the sy

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Nalla, Jagan. "Data Engineering Paradigms for Real-Time Network Threat Detection: A Framework for Scalable Security Analytics." European Journal of Computer Science and Information Technology 13, no. 40 (2025): 62–74. https://doi.org/10.37745/ejcsit.2013/vol13n406274.

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This article explores the critical intersection of data engineering and cybersecurity, focusing on architectural approaches for network threat detection at scale. As organizations face increasingly sophisticated cyber threats, traditional security tools struggle with the volume and velocity of network data. A comprehensive framework for building scalable data pipelines effectively ingests, processes, and analyzes network flow data for security monitoring. Event-driven architectures utilizing technologies such as Kafka for real-time data streaming, Flink for implementing complex detection logic

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Barik, Rabindra K., Rojalina Priyadarshini, Rakesh K. Lenka, Harishchandra Dubey, and Kunal Mankodiya. "Fog Computing Architecture for Scalable Processing of Geospatial Big Data." International Journal of Applied Geospatial Research 11, no. 1 (2020): 1–20. http://dx.doi.org/10.4018/ijagr.2020010101.

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Geospatial data analysis using cloud computing platform is one of the promising areas for analysing, retrieving, and processing volumetric data. Fog computing paradigm assists cloud platform where fog devices try to increase the throughput and reduce latency at the edge of the client. In this research paper, the authors discuss two case studies on geospatial data analysis using Fog-assisted cloud computing namely, (1)Ganga River Basin Management System; and (2)Tourism Information Management of India. Both case studies evaluate proposed GeoFog architecture for efficient analysis and management

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Rana, Nirav Pravinsinh. "Architectural Patterns for Building Scalable Enterprise Forecasting Platforms." European Journal of Computer Science and Information Technology 13, no. 39 (2025): 107–21. https://doi.org/10.37745/ejcsit.2013/vol13n39107121.

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The architecture of modern enterprise forecasting platforms incorporates sophisticated components for managing hierarchical data structures, real-time collaboration, and dynamic scaling capabilities. These platforms address challenges in multi-channel inventory management, data synchronization, and forecast accuracy through innovative cloud technologies and architectural patterns. The implementation demonstrates significant improvements in synchronization speed, response times, and forecast accuracy while maintaining data consistency across distributed systems. The integration of advanced secu

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Li, Feifei, and Suman Nath. "Scalable data summarization on big data." Distributed and Parallel Databases 32, no. 3 (2014): 313–14. http://dx.doi.org/10.1007/s10619-014-7145-y.

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Vinay Siva Kumar Bhemireddy. "Understanding microservices architecture: Building scalable and resilient systems." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 2348–62. https://doi.org/10.30574/wjaets.2025.15.3.1139.

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Microservices architecture represents a paradigm shift in software design, breaking monolithic applications into independently deployable services with clear boundaries and responsibilities. This article explores the fundamental principles of microservices, tracing their evolution from service-oriented architecture while examining strategic decomposition methodologies, domain modeling, and API design. It investigates communication patterns between services, comparing synchronous and asynchronous models, and addresses the challenges of distributed data management through patterns like event sou

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Cavalin, P. R., M. A. C. Gatti, T. G. P. Moraes, et al. "A scalable architecture for real-time analysis of microblogging data." IBM Journal of Research and Development 59, no. 2/3 (2015): 16:1–16:10. http://dx.doi.org/10.1147/jrd.2015.2408911.

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Jia, Wen-Kang. "A Scalable Multicast Source Routing Architecture for Data Center Networks." IEEE Journal on Selected Areas in Communications 32, no. 1 (2014): 116–23. http://dx.doi.org/10.1109/jsac.2014.140111.

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Elahi, Bukhtawar, Asad Waqar Malik, Anis U. Rahman, and Muazzam A. Khan. "Toward scalable cloud data center simulation using high‐level architecture." Software: Practice and Experience 50, no. 6 (2019): 827–43. http://dx.doi.org/10.1002/spe.2769.

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Lokugam Hewage, Chamin Nalinda, Debra F. Laefer, Anh-Vu Vo, Nhien-An Le-Khac, and Michela Bertolotto. "Scalability and Performance of LiDAR Point Cloud Data Management Systems: A State-of-the-Art Review." Remote Sensing 14, no. 20 (2022): 5277. http://dx.doi.org/10.3390/rs14205277.

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Current state-of-the-art point cloud data management (PCDM) systems rely on a variety of parallel architectures and diverse data models. The main objective of these implementations is achieving higher scalability without compromising performance. This paper reviews the scalability and performance of state-of-the-art PCDM systems with respect to both parallel architectures and data models. More specifically, in terms of parallel architectures, shared-memory architecture, shared-disk architecture, and shared-nothing architecture are considered. In terms of data models, relational models, and nov

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ZIPPEL, RICHARD. "THE DATA STRUCTURE ACCELERATOR ARCHITECTURE." International Journal of High Speed Electronics and Systems 07, no. 04 (1996): 533–71. http://dx.doi.org/10.1142/s012915649600030x.

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We present a heterogeneous architecture that contains a fine grained, massively parallel SIMD component called the data structure accelerator and demonstrate its use in a number of problems in computational geometry including polygon filling and convex hull. The data structure accelerator is extremely dense and highly scalable. Systems of 106 processing elements can be embedded in workstations and personal computers, without dramatically changing their cost. These components are intended for use in tandem with conventional single sequence machines and with small scale, shared memory multiproce

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Urvangkumar, Kothari. "A Comprehensive Data Governance Framework for Multi-Source Data Systems in the Utility." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 6 (2019): 1–9. https://doi.org/10.5281/zenodo.14980764.

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This paper presents a full data governance framework to manage multi-source data systems in the utility industry. The framework incorporates advanced methods to solve data heterogeneity, security vulnerabilities and regulatory compliance. It introduces three key innovations: (1) a cross-domain data unification model using ISO/TC 211 geospatial standards and master data management principles to harmonize multiple utility data sources; (2) a privacy-preserving architecture combining Byzantine-tolerant aggregation and differential privacy mechanisms to get 95% data utility while re-identification

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Rawish Siddiqui, Muhammad. "Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond." Chemistry Research and Practice 1, no. 2 (2024): 01–06. https://doi.org/10.64030/3065-906x.01.02.04.

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In the age of digital transformation, the rise of Big Data has fundamentally altered how organizations store, process, and utilize information. This whitepaper provides a comprehensive analysis comparing Big Data with traditional data systems, data warehousing, business intelligence (BI), artificial intelligence (AI), data science, and NoSQL databases. By exploring key differentiators such as volume, variety, velocity, and processing capabilities, this paper aims to shed light on how Big Data has reshaped modern technology infrastructures and its role in advancing analytics, decision-making, a

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Belli, Laura, Simone Cirani, Luca Davoli, et al. "A Scalable Big Stream Cloud Architecture for the Internet of Things." International Journal of Systems and Service-Oriented Engineering 5, no. 4 (2015): 26–53. http://dx.doi.org/10.4018/ijssoe.2015100102.

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The Internet of Things (IoT) will consist of billions (50 billions by 2020) of interconnected heterogeneous devices denoted as “Smart Objects:” tiny, constrained devices which are going to be pervasively deployed in several contexts. To meet low-latency requirements, IoT applications must rely on specific architectures designed to handle the gigantic stream of data coming from Smart Objects. This paper propose a novel Cloud architecture for Big Stream applications that can efficiently handle data coming from Smart Objects through a Graph-based processing platform and deliver processed data to

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Skliarenko, Olena, Yaroslav Savchenko, Leonid Lytvynenko, and Orest Sushynskyi. "ARCHITECTURAL APPROACHES TO THE DEVELOPMENT OF SCALABLE WEB APPLICATIONS." Cybersecurity: Education, Science, Technique 4, no. 24 (2024): 341–50. http://dx.doi.org/10.28925/2663-4023.2024.24.341350.

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This article explores modern methods and technologies for creating scalable web applications. The need for such systems is constantly growing due to the increase in data volumes and the number of users, which requires high performance and reliability. This article is devoted to the study of modern methods of scaling web applications, which is becoming one of the most pressing problems of modern programming due to the rapid growth of data and the number of users. Scalability determines the ability of a system to efficiently handle an increasing load by adding resources (processors, memory, serv

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Dileep Domakonda. "Secure and Scalable Microservices Architecture : Principles, Benefits, and Challenges." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1897–902. https://doi.org/10.32628/cseit23112569.

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Microservices architecture is one approach to structuring applications as a collection of small, independently deployable services interacting via APIs, which improves modularity, scalability, and fault isolation. Microservices provide better resilience, deployment flexibility, and utilization of resources compared to monolithic architectures, making them a perfect fit for cloud-native applications. In today's paper, we discuss fundamental principles such as independent deployment, decoupling, fault tolerance, and technology agnosticism while considering challenges such as inter-service commun

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