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Journal articles on the topic 'Cloud Data Warehousing'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Ma, Hui, Klaus-Dieter Schewe, Bernhard Thalheim, and Qing Wang. "Cloud Warehousing." JUCS - Journal of Universal Computer Science 17, no. (8) (2011): 1183–201. https://doi.org/10.3217/jucs-017-08-1183.

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Data warehouses integrate and aggregate data from various sources to support decision making within an enterprise. Usually, it is assumed that data are extracted from operational databases used by the enterprise. Cloud warehousing relaxes this view permitting data sources to be located anywhere on the world-wide web in a so-called "cloud", which is understood as a registry of services. Thus, we need a model of dataintensive web services, for which we adopt the view of the recently introduced model of abstract state services (AS2s). An AS2 combines a hidden database layer with an operation-equi

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Chandrakanth, Lekkala. "Cloud-Based Data Warehousing Optimization Techniques." Journal of Scientific and Engineering Research 9, no. 5 (2022): 114–18. https://doi.org/10.5281/zenodo.12789974.

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This article delves into enhancing cloud-based data warehousing's efficiency with the accompanying expert on Snowflake and Amazon Web Services (AWS). Companies are relying more and more on cloud systems for storing and analyzing data, and optimizing data warehousing now seems to be a really important part for performing well during queries, cutting down data storage costs, and managing data in a good way. This study delivers a case study that describes the optimization methods utilized in a Snowflake installation on AWS. This approach leads to performance improvements and cost savings. The opt

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Suresh Kumar Somayajula. "Demystifying Modern Data Warehousing: From Traditional to Cloud-Native Solutions." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 348–62. https://doi.org/10.32628/cseit25111235.

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The article explores the transformative landscape of cloud-native data warehousing, investigating the paradigm shift from traditional on-premises infrastructures to advanced cloud-based architectures. Through comprehensive analysis across diverse industry sectors, the study illuminates the profound technological and operational metamorphosis enabled by cloud data warehouse solutions. By examining performance metrics, resource utilization, security frameworks, and organizational capabilities, the article provides critical insights into how modern data management technologies transcend historica

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Urvangkumar, Kothari. "Exploring the Convergence of Cloud Computing and Data Warehousing for Smarter Technology Solutions." International Journal of Leading Research Publication 6, no. 4 (2025): 1–13. https://doi.org/10.5281/zenodo.15125151.

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The paper analyzes cloud computing solutions that boost data warehouses through enhanced scalability combined with cost optimization features together with real-time analytic capabilities. Data warehousing systems that operate from on-site locations struggle with expenses that are high and encounter limitations concerning scalability together with slow processing of data. Organizations choose cloud-based data warehousing solutions because they obtain scalable management capabilities for large datasets which help decrease their infrastructure expenses. The research investigates the cloud-native

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Srinivasa, Chakravarthy Seethala. "Cloud and AI Convergence in Banking & Finance Data Warehousing: Ensuring Scalability and Security." European Journal of Advances in Engineering and Technology 9, no. 3 (2022): 190–92. https://doi.org/10.5281/zenodo.14168767.

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In the banking and finance sector, the integration of cloud computing and artificial intelligence (AI) technologies within data warehousing solutions is revolutionizing data management, processing, and security. This convergence is essential not only for handling complex datasets but also for meeting the growing demands for scalability and enhanced security—both critical to modern financial systems. This article examines how cloud-AI fusion addresses unique challenges in banking data warehousing, focusing on strategies to ensure scalability and secure sensitive financial data. By explori

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Ahmadi, Sina. "Elastic Data Warehousing: Adapting To Fluctuating Workloads With Cloud-Native Technologies." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 3 (2023): 282–301. http://dx.doi.org/10.60087/jklst.vol2.n3.p301.

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This research focuses on the development of elastic data warehousing while adapting to changing workloads with the help of cloud-based technologies. The traditional methods of data warehousing need innovative and creative strategies in order to improve their efficiency. Thus, this research focuses on analyzing innovative methods which can improve the future of data warehousing, such as machine learning, encryption, artificial intelligence, etc. Moreover, the study also focuses on specific industries that require customized solutions to data warehousing. These include the manufacturing, finance

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Kumar Singu, Santosh. "Migration Strategies for Legacy Data Warehousing Systems to Cloud Platforms." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 2164–67. http://dx.doi.org/10.21275/sr231207111537.

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Venkata, Tadi. "Performance and Scalability in Data Warehousing: Comparing Snowflake's Cloud-Native Architecture with Traditional On-Premises Solutions Under Varying Workloads." European Journal of Advances in Engineering and Technology 9, no. 5 (2022): 127–39. https://doi.org/10.5281/zenodo.13319605.

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This study investigates the performance and scalability of Snowflake's cloud-native architecture compared to traditional on-premises data warehousing solutions under varying workloads. As organizations increasingly migrate to cloud-based platforms for their data management needs, understanding the trade-offs and benefits of such transitions becomes crucial. This research provides a comprehensive analysis of Snowflake's data processing speed and scalability capabilities, examining its efficiency in handling diverse and intensive workloads. By employing a series of benchmark tests and performanc

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Aneeshkumar Perukilakattunirappel Sundareswaran, Swamy Sai Krishna Kireeti Athamakuri, Khushmeet Singh, and Rajeev Kumar Sharma. "AI-Driven Data Quality Assurance in Multi-Cloud Data Warehousing Environments." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 07 (2025): 3135–41. https://doi.org/10.47392/irjaeh.2025.0462.

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Multi-cloud data warehousing has emerged as a critical enabler for organizations seeking enhanced agility, scalability, and resilience in today’s rapidly evolving data-driven and cloud-native environments. Being subjected to various cloud platforms makes inconsistencies, latency, duplication, and governance imbalances harder to maintain and oversee, which is considered a significant problem today. This study aims to keep data quality across the cloud by developing an AI-driven data quality strategy. This framework employs a machine learning model that identifies, categorizes, and corrects data

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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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Srinivasa, Chakravarthy Seethala. "Leveraging AI in Cloud Data Warehouses for Manufacturing: A Future-Proof Approach." Journal of Scientific and Engineering Research 5, no. 6 (2018): 401–3. https://doi.org/10.5281/zenodo.14059537.

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The manufacturing sector is undergoing a profound digital transformation, driven by the convergence of artificial intelligence (AI), cloud computing, and advanced data warehousing techniques. This paper examines the transformative potential of AI-powered cloud data warehouses in modernizing manufacturing operations to enable real-time insights and predictive capabilities. We explore how these technologies address key challenges in the manufacturing industry, including supply chain optimization, predictive maintenance, quality control, and demand forecasting. Our findings suggest that the integ

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Srikanth Gangarapu and Vishnu Vardhan Reddy Chilukoori. "The Future of Data Warehousing: Trends, Technologies, and Challenges in the Era of Big Data, Cloud Computing, and Artificial Intelligence." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 470–79. http://dx.doi.org/10.32628/cseit241051029.

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This article explores the future of data warehousing, discussing emerging trends, technologies, and challenges shaping the landscape of data management and analytics in the era of big data, cloud computing, and artificial intelligence. It delves into topics such as the rise of cloud data warehouses, the increasing adoption of artificial intelligence and machine learning in data warehousing for advanced analytics and automation, the growing importance of data governance, privacy, and security in the face of evolving regulations, and the need for real-time data processing and analytics to suppor

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Suganyadevi, Dr S., and Priyadharshini J G. "REVIEW ON DATA WAREHOUSING." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–9. https://doi.org/10.55041/isjem02474.

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Data warehousing is a fundamental component of modern business intelligence (BI) systems, enabling organizations to store, manage, and analyze vast amounts of data for strategic decision-making. It involves the integration of data from disparate sources into a centralized repository, optimized for efficient querying and reporting. The process of Extract, Transform, and Load (ETL) ensures that data is cleaned, transformed, and loaded into the warehouse for consistent and high-quality analysis. By leveraging data warehouses, businesses gain valuable insights into historical trends, optimize oper

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Researcher. "DATA WAREHOUSING WITH AMAZON REDSHIFT: REVOLUTIONIZING BIG DATA ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 395–405. https://doi.org/10.5281/zenodo.13270530.

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The article talks about Amazon Redshift, a cutting-edge cloud-based data warehouse that is changing the way big data analytics is done. In it, the architecture, main features, and benefits of Redshift are discussed in detail. Columnar storage, massively parallel processing, and a distributed system design are emphasized. The article discusses how business intelligence, data science, operational analytics, customer analytics, and financial analytics are used in the real world. It also compares and contrasts with other cloud data stores, such as Snowflake and Google BigQuery, pointing out their

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Ahmadi, Sina. "Optimizing Data Warehousing Performance through Machine Learning Algorithms in the Cloud." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 1859–67. http://dx.doi.org/10.21275/sr231224074241.

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Seethala, Srinivasa Chakravarthy. "CLOUD-BASED DATA WAREHOUSING IN ENERGY & UTILITIES: LEVERAGING AI FOR SCALABLE SOLUTIONS." CLOUD-BASED DATA WAREHOUSING IN ENERGY & UTILITIES: LEVERAGING AI FOR SCALABLE SOLUTIONS 1, no. 10 (2016): 255–60. https://doi.org/10.5281/zenodo.14617718.

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The energy and utilities sector is experiencing significant transformation driven by theintegration of cloud-based data warehousing and artificial intelligence (AI). This paperexplores the potential of these technologies in tackling major industry challenges, includinginfrastructure modernization, energy consumption optimization, market volatility, and thetransition to sustainable energy solutions. Cloud-based data warehousing provides a scalableplatform for managing large quantities of operational and consumption data, while AIleverages this data to deliver actionable insights. Key applicatio

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Kandula, Nagababu. "Evolution and Impact of Data Warehousing in Modern Business and Decision Support Systems." International Journal of Computer Science and Data Engineering 2, no. 2 (2025): 1–11. https://doi.org/10.55124/csdb.v2i2.247.

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Data warehousing has become an essential tool in modern organizations driven by increasing business complexity and technological advancements. Organizations collect vast amounts of data from multiple sources that require efficient storage and analysis solutions. This research paper examines the role of data warehousing in decision making, its integration with emerging technologies, and its growing impact on various industries. Research significance: This research is significant as it highlights the transformative role of data warehousing in decision-making across industries. By improving data

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Winter, Christian, Jana Giceva, Thomas Neumann, and Alfons Kemper. "On-demand state separation for cloud data warehousing." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2966–79. http://dx.doi.org/10.14778/3551793.3551845.

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Moving data analysis and processing to the cloud is no longer reserved for a few companies with petabytes of data. Instead, the flexibility of on-demand resources is attracting an increasing number of customers with small to medium-sized workloads. These workloads do not occupy entire clusters but can run on single worker machines. However, picking the right worker for the job is challenging. Abstracting from worker machines, e.g., using stateless architectures, introduces overheads impacting performance. Solutions without stateless architectures resort to query restarts in the event of an adv

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Qwaider, Walid Qassim. "Data Warehousing based on a Cloud Computing Architecture." International Journal of Computer Applications 185, no. 42 (2023): 7–10. http://dx.doi.org/10.5120/ijca2023923226.

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Ramesh, Betha. "The Rise of Cloud Data Warehousing: AWS Redshift vs Snowflake." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 5, no. 6 (2019): 1–9. https://doi.org/10.5281/zenodo.14866601.

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The emergence of cloud data warehousing has fundamentally transformed how organizations manage and analyze their data assets. This paper presents a comprehensive analysis of two leading cloud data warehouse solutions: Amazon Redshift and Snowflake. Through detailed examination of their architectures, performance characteristics, and economic models, we provide insights into the evolving landscape of cloud-based analytics. Our analysis reveals the distinct approaches these platforms take to address modern data warehousing challenges, including scalability, concurrency, and data sharing. The fin

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Rajesh Kumar Srirangam. "The Growing Trend of Cloud-Based Data Integration and Warehousing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 596–600. http://dx.doi.org/10.32628/cseit241051042.

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This article examines the growing trend of cloud-based data integration and warehousing solutions in response to the exponential growth of data generation and the need for scalable, flexible analytics capabilities. It explores the key drivers behind cloud adoption, including data volume increases, demand for real-time insights, cost efficiencies, and advanced analytics requirements. The benefits of cloud solutions are discussed, such as scalability, cost-effectiveness, agility, and enhanced analytical capabilities. The article also addresses challenges like data security, vendor lock-in, and p

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Mahesh Thoutam. "Comparative Analysis of Data Warehousing Solutions in the Cloud: A Focus on Azure PostgreSQL." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 423–31. http://dx.doi.org/10.32628/cseit241051016.

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This article provides a comprehensive analysis of cloud data warehousing solutions, with a focus on Azure PostgreSQL. It examines the rapidly growing cloud data warehouse market, highlighting key advantages such as scalability, cost-effectiveness, and advanced analytics capabilities. The article compares major platforms including Azure PostgreSQL, Amazon Redshift, Google BigQuery, and Snowflake, detailing their features, strengths, and market positions. Special attention is given to Azure PostgreSQL, outlining scenarios where it excels and providing best practices for leveraging its features.

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Oluwademilade, Aderemi Agboola, Chukwuemeke Uzoka Abel, Oluwaseun Ajayi Olanrewaju, Chibunna Ubanadu Bright, Ifesinachi Daraojimba Andrew, and Elizabeth Alozie Chisom. "Transforming Supply Chain Analytics with Real-Time Data and Cloud Data Warehousing: A Strategic Framework." Engineering and Technology Journal 10, no. 05 (2025): 5029–39. https://doi.org/10.5281/zenodo.15461534.

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This paper explores the transformative role of real-time data and cloud data warehousing in optimizing supply chain analytics. As supply chains grow increasingly complex, businesses are increasingly turning to data-driven strategies to enhance operational efficiency, reduce costs, and improve responsiveness to market demands. Real-time data provides the foundation for proactive decision-making, enabling organizations to monitor supply chain activities continuously and make informed adjustments to minimize disruptions. The integration of cloud data warehousing offers scalable, flexible, and cos

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Srinivasa, Rao Karanam. "The Evolution of Data Warehousing: From On-Premise to Cloud-Native Solutions." Journal of Advances in Developmental Research 15, no. 2 (2024): 1–9. https://doi.org/10.5281/zenodo.15206387.

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Throughout the broader timeline of enterprise computing, data warehousing has become an integral approach for consolidating disparate data sets into the centralized, structured repository. Initial on-premise models emphasized intricately planned schema designs and hardware provisioning, but with the advent of highly scalable Cloud infrastructures, the complexities of deployment and management began to shift drastically. This paper evaluates the transitions from historical on-premises architecture, which demanded massive capital outlays, into more flexible cloud-based data warehouse topologies

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Shi, Yadong, Jiaqiang Yuan, Peiyuan Yang, Yufu Wang, and Zhou Chen. "Implementing intelligent predictive models for patient disease risk in cloud data warehousing." Applied and Computational Engineering 67, no. 1 (2024): 34–40. http://dx.doi.org/10.54254/2755-2721/67/2024ma0059.

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A data warehouse, which stores data after it has been extracted, processed, and organized into files and folders, is a cloud data warehousing solution for storing structured data from one or more sources. When data is stored in an organized format within files and folders, it becomes easily accessible and facilitates strategic, data-driven decision-making. This paper introduces the importance of cloud data warehousing in medical information management and discusses its application in a disease prediction model. By analyzing medical data and constructing predictive models, it can assist medical

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Shi, Yadong, Jiaqiang Yuan, Peiyuan Yang, Yufu Wang, and Zhou Chen. "Implementing intelligent predictive models for patient disease risk in cloud data warehousing." Applied and Computational Engineering 77, no. 1 (2024): 271–77. https://doi.org/10.54254/2755-2721/77/2024ma0059.

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A data warehouse, which stores data after it has been extracted, processed, and organized into files and folders, is a cloud data warehousing solution for storing structured data from one or more sources. When data is stored in an organized format within files and folders, it becomes easily accessible and facilitates strategic, data-driven decision-making. This paper introduces the importance of cloud data warehousing in medical information management and discusses its application in a disease prediction model. By analyzing medical data and constructing predictive models, it can assist medical

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Sai, Kishore Chintakindhi. "Dynamic Cost Optimization Framework for BigQuery and Cloud Data Warehousing Systems." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 11, no. 1 (2025): 1–24. https://doi.org/10.5281/zenodo.15564484.

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This dissertation introduces a cost optimization framework designed for BigQuery and cloud data warehouses, focusing on the issue of growing operational costs related to data processing and storage. Using extensive usage and performance data from various cloud environments, this research pinpoints key cost factors and formulates strategies for improved resource allocation, resulting in considerable cost savings. The results generally indicate that adaptive resource management methods can lower operational costs by as much as 30%, thus boosting the long-term financial viability of cloud data wa

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Basani, Maria Anurag Reddy. "Optimizing Cloud Data Storage: Evaluating File Formats for Efficient Data Warehousing." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 922–31. http://dx.doi.org/10.22214/ijraset.2024.64753.

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This paper presents a detailed analysis of three widely-used data storage formats—Parquet, Avro, and ORC— evaluating their performance across key metrics such as query execution, compression efficiency, data skipping, schema evolution, and throughput. Each format offers distinct advantages depending on the nature of the workload. Parquet is optimized for read-heavy analytical queries, providing excellent compression and efficient query performance through its columnar structure. Avro excels in write-heavy, real-time data streaming scenarios, where schema flexibility and backward compatibility

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Yang, Peiyuan, Zhou Chen, Guangze Su, Han Lei, and Baoming Wang. "Enhancing traffic flow monitoring with machine learning integration on cloud data warehousing." Applied and Computational Engineering 67, no. 1 (2024): 15–21. http://dx.doi.org/10.54254/2755-2721/67/2024ma0058.

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With urbanization and rising vehicle ownership rates, global traffic congestion and accidents have become pressing issues. This paper delves into leveraging cloud data warehousing and machine learning to tackle traffic flow monitoring and prediction, along with their potential in intelligent transportation systems. Through comprehensive analysis and case studies, it highlights how modern technology can enhance urban traffic management and services. Initially, the paper underscores the importance of traffic monitoring and prediction, identifying shortcomings in traditional approaches and advoca

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Yang, Peiyuan, Zhou Chen, Guangze Su, Han Lei, and Baoming Wang. "Enhancing traffic flow monitoring with machine learning integration on cloud data warehousing." Applied and Computational Engineering 77, no. 1 (2024): 238–44. https://doi.org/10.54254/2755-2721/77/2024ma0058.

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With urbanization and rising vehicle ownership rates, global traffic congestion and accidents have become pressing issues. This paper delves into leveraging cloud data warehousing and machine learning to tackle traffic flow monitoring and prediction, along with their potential in intelligent transportation systems. Through comprehensive analysis and case studies, it highlights how modern technology can enhance urban traffic management and services. Initially, the paper underscores the importance of traffic monitoring and prediction, identifying shortcomings in traditional approaches and advoca

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Samyukta Rongala. "Optimizing ETL Processes for High-Volume Data Warehousing in Financial Applications." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 700–708. https://doi.org/10.52783/jisem.v10i8s.1130.

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The Extract, Transform, Load (ETL) process is a critical backbone in financial data warehousing, where large-scale data volumes demand optimized performance to meet industry requirements. Financial institutions rely heavily on ETL systems to integrate, cleanse, and structure data for decision-making and regulatory compliance. This paper delves into the optimization of ETL processes for high-volume data warehousing in financial applications. By analyzing current challenges, exploring advanced architectures, and incorporating emerging technologies such as Big Data frameworks and cloud solutions,

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Mahashabde, Saumya, and Sudeepta Banerjee. "Data Warehousing in the Cloud: Unveiling the Advantages and Challenges for Modern Organizations." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1299–304. http://dx.doi.org/10.21275/sr231014081929.

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Cheng, Zehao. "Research on Integration and Optimization of Intelligent Warehouse and Logistics Information System in E-commerce Supply Chain." Highlights in Science, Engineering and Technology 138 (May 11, 2025): 157–62. https://doi.org/10.54097/3e3x1179.

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The traditional warehousing and logistics management model has been difficult to meet the needs of efficient operation, and the integration and optimization of intelligent warehousing and logistics information system has become the key. In this paper, an integrated optimization scheme of intelligent warehousing and logistics information system based on Internet of Things (IoT), big data, cloud computing and artificial intelligence (AI) is proposed. By designing a four-layer system integration architecture (data acquisition layer, data processing and analysis layer, application service layer an

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Parth Vyas. "Demystifying Dimensional Modeling for Modern Data Warehousing." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 174–80. https://doi.org/10.32996/jcsts.2025.7.2.16.

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This article demystifies dimensional modeling for data warehousing professionals by breaking down complex concepts into accessible components. It explores the foundational elements of dimensional design—fact tables, dimension tables, and star schemas—while delving into advanced topics like slowly changing dimensions, conformed dimensions, and hierarchical structures. The article examines implementation considerations, including surrogate keys versus natural keys, star versus snowflake schemas, and aggregation strategies that impact performance. It demonstrates how dimensional modeling principl

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Ram, A. Sai. "Explicitly Disclosing Clients Illness Catalogue Using Data Science Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1504–8. http://dx.doi.org/10.22214/ijraset.2021.38658.

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Abstract: Across the world in our day-to-day life, we come across various medical inaccuracies caused due to unreliable patient’s reminiscence. Statistically, communication problems are the most significant aspect that hampers the diagnosis of patient’s diseases. So, this paper represents the best theoretical solution to achieve patient care in the most adequate way. In these pandemic days, the communication gap between the patient and the physician has begun to decline to a nominal level. This paper demonstrates a vital solution and a steppingstone to the complete digitalization of the client

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Singh, Baljit. "SAP Datasphere – Business User-Oriented Approach to Cloud Data Warehousing." International Journal of Computer Trends and Technology 71, no. 5 (2023): 1–4. http://dx.doi.org/10.14445/22312803/ijctt-v71i5p101.

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Agboola, Oluwademilade Aderemi, Abel Chukwuemeke Uzoka, Abraham Ayodeji Abayomi, Jeffrey Chidera Ogeawuchi, Ejielo Ogbuefi, and Samuel Owoade. "Systematic Review of Best Practices in Data Transformation for Streamlined Data Warehousing and Analytics." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 2 (2023): 687–94. https://doi.org/10.54660/.ijmrge.2023.4.2.687-694.

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This systematic review explores best practices in data transformation for streamlined data warehousing and analytics. As organizations continue to generate vast amounts of data, transforming and warehousing it efficiently becomes critical to support data-driven decision-making. The review discusses key practices, including the automation of data transformation processes, ensuring data quality and integrity, and optimizing scalability and performance. Automation, particularly through ETL/ELT tools, reduces manual errors and enhances operational efficiency, while data quality practices ensure re

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Praveen, Borra. "Snowflake: A Comprehensive Review of a Modern Data Warehousing Platform." International Journal of Computer Science and Information Technology Research (IJCSITR) 3, no. 1 (2022): 11–16. https://doi.org/10.5281/zenodo.11617628.

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<em>Snowflake represents a state-of-the-art data warehousing platform, fundamentally altering the landscape of data management within cloud environments. This paper offers an exhaustive examination of Snowflake, delving into its structural intricacies, pivotal functionalities, competitive edges, and far-reaching implications for the sector. By meticulously scrutinizing its inventive methodologies for data retention, processing, and adaptability, this analysis elucidates Snowflake's rise to prominence as a leader in cloud-centric data management solutions. Moreover, it sheds light on Snowflake'

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Bao, Pengnan, Jinyuan Liu, and Yuxin Zhang. "Optimizing Warehousing Strategies for Retail Food Live Streaming on Supply Chain Cost: A Case Study of TikTok." Highlights in Business, Economics and Management 28 (April 9, 2024): 137–43. http://dx.doi.org/10.54097/8xs8rd51.

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The outbreak has indeed left a significant impact on the traditional offline sales model. However, it has simultaneously catalyzed the flourishing growth of the e-commerce live-streaming industry. Consumers have progressively adapted to this novel online shopping mode, signifying a paradigm shift in retail dynamics. Therefore, this paper studies the cost problem under different strategic choices of logistics and warehousing for retail food from the perspective of a platform system and analyzes the after-sale cost of a two-level retail food supply chain consisting of a manufacturer and an e-com

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Researcher. "MIGRATING ON-PREM DATA WAREHOUSING TO MICROSOFT FABRIC: LESSONS LEARNED AND BEST PRACTICES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 682–93. https://doi.org/10.5281/zenodo.13884754.

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This article explores the complex process of migrating on-premises data warehousing to Microsoft Fabric, a comprehensive cloud analytics platform. It examines the key drivers behind cloud adoption in data management, including scalability, cost-efficiency, and advanced analytics capabilities. The article delves into the major challenges organizations face during migration, such as data transfer complexities, integration with existing systems, and ensuring data security and compliance. It provides best practices for successful migration, including conducting thorough pre-migration assessments,

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Researcher. "DATA WAREHOUSING ARCHITECTURE AND IMPLEMENTATION FOR ENHANCED FINANCIAL REPORTING: A SYSTEMATIC REVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 751–59. https://doi.org/10.5281/zenodo.14228481.

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This article presents a comprehensive analysis of data warehousing architectures and implementations specifically designed for financial reporting systems. The article examines the evolution, current state, and future trends of data warehousing in financial institutions, focusing on architectural components, implementation frameworks, and business intelligence integration. Through detailed analysis of system design principles, performance optimization techniques, and regulatory compliance requirements, we demonstrate how modern data warehouses can effectively support complex financial rep

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Satyadeepak, Bollineni. "Comparative analysis of Databricks and Traditional Data Warehousing Solutions." Journal of Scientific and Engineering Research 11, no. 2 (2024): 228–33. https://doi.org/10.5281/zenodo.14273128.

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This paper comprehensively compares Databricks and traditional data warehousing solutions regarding their architectures, performance metrics, cost implication, and user experience. Indeed, in an era when organizations depend on data to make strategic decisions, understanding different data management platforms should be invited. Databricks is an Apache Spark-built, cloud-based, state-of-the-art solution offering speed, scalability, and real-time analytics, making it the best solution for modern-day data analysis. On the other hand, traditional data warehousing solutions can be up to the task b

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Kumar Mally, Prashanth. "Cloud Data Warehousing and AI Analytics: A Comprehensive Review of Literature." International Journal of Computer Trends and Technology 71, no. 10 (2023): 28–38. http://dx.doi.org/10.14445/22312803/ijctt-v71i10p104.

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Mohite, Ganesh, Rushikesh Bhagat, and Roshan Jadhav. "Big Data Analysis using Cloud Computing: Opportunities, Challenges and Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2062–66. https://doi.org/10.22214/ijraset.2025.68353.

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Abstract: Big Data and cloud computing have revolutionized data storage, processing, and analysis, enabling businesses and industries to manage vast volumes of data efficiently. Cloud computing provides scalable infrastructure, cost-effective storage solutions, and real-time analytics capabilities, making it an essential platform for Big Data applications. This study explores the opportunities, challenges, and applications of Big Data analysis in cloud environments, highlighting key technologies such as Hadoop, Spark, and cloud-based data warehousing solutions. The research identifies major ch

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Dheeraj Kumar Bansal. "Enterprise Data Engineering: Architecting Modern Data Warehouses for Business Success." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 3266–77. https://doi.org/10.32628/cseit251112348.

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This article provides a comprehensive overview of Enterprise Data Engineering with a focus on Data Warehouse Architecture, exploring its critical role in modern organizations' data strategies. It delves into the complexities of integrating diverse data sources, including point-of-sale systems, online databases, and CRM platforms. It examines the nuances of ETL/ELT processes essential for data integration. The article discusses various data warehouse architectures, compares cloud-based and on-premises solutions, and elaborates on dimensional modeling techniques crucial for effective data organi

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Bhardwaj, Abhijeet, Nagender Yadav, Jay Bhatt, Om Goel, Punit Goel, and Arpit Jain. "Leveraging SAP BW4HANA for Scalable Data Warehousing in Large Enterprises." Integrated Journal for Research in Arts and Humanities 4, no. 6 (2024): 143–63. https://doi.org/10.55544/ijrah.4.6.13.

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In the era of big data, large enterprises face the challenge of efficiently managing and analyzing vast volumes of information to derive actionable insights. SAP BW/4HANA emerges as a powerful solution that facilitates scalable data warehousing, enabling organizations to harness their data for strategic decision-making. This paper explores the key features and advantages of SAP BW/4HANA, highlighting its capability to integrate real-time data processing, advanced analytics, and enhanced data modeling. By leveraging the in-memory computing power of HANA, enterprises can significantly reduce dat

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Preeta Pillai. "Cloud vs. On-Premise Data Warehousing: A Strategic Analysis for Financial Institutions." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 503–13. https://doi.org/10.32996/jcsts.2025.7.3.57.

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The transformation of data warehousing in financial services marks a pivotal shift in how institutions manage and utilize data assets. Financial organizations navigate complex decisions between cloud-based, on-premise, and hybrid solutions, each offering distinct advantages and challenges. The evolution encompasses enhanced security protocols, improved regulatory compliance mechanisms, and advanced analytical capabilities. Modern implementations demonstrate substantial improvements in operational efficiency, cost optimization, and system performance. The integration of artificial intelligence

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Kumar, Rohit. "AI-Augmented Data Security in Cloud Migration: Leveraging Generative AI and Snowflake for Secure Financial Data Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–8. https://doi.org/10.55041/ijsrem50146.

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This paper offers a complete framework combining Snowflake's cloud data platform with AI- augmented methods to improve data security during cloud migration. Six strategic phases—from preprocessing to evaluation—each help to contribute to better performance measures in the suggested approach. Visual studies show a notable decrease in system response time (250 ms to 140 ms) as well as a continuous increase in security score (70% to 95%), and detection accuracy (68% to 94%). Moreover, accuracy and precision measures show clear development throughout the phases, reaching respectively 93% and 91%.

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Thite, Gururaj. "Modern Data Architectures in Financial Analytics: A Technical Deep Dive." European Journal of Computer Science and Information Technology 13, no. 22 (2025): 79–86. https://doi.org/10.37745/ejcsit.2013/vol13n227986.

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Modern financial analytics architectures are undergoing a transformative evolution in response to increasing data complexity and volume demands. The integration of distributed computing frameworks, cloud-based data warehousing solutions, and artificial intelligence has revolutionized how financial institutions process and analyze data. Advanced ETL pipelines leveraging Apache Spark's capabilities have enhanced processing efficiency, while Snowflake's cloud platform has optimized query performance through innovative storage and compute separation. AI-driven quality assurance frameworks have aut

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Kishore Ande. "The Modern Data Engineer: A Comprehensive Guide to ETL, MDM, and Cloud Data Solutions." International Journal for Research Publication and Seminar 16, no. 1 (2025): 344–62. https://doi.org/10.36676/jrps.v16.i1.141.

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The position of data engineers has been advanced substantially over the last ten years with the evolution of Extract, Transform, Load (ETL) processes, Master Data Management (MDM), and cloud data platforms. In spite of the quicker uptake of cloud technology and automating ETL processes, numerous gaps remain in managing, processing, and quality checking large amounts of data efficiently in the modern enterprise landscape. Past research indicates there is a requirement for more efficient ways to integrate heterogeneous data sources, resolve data quality problems, and facilitate smooth interactio

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