Gotowe bibliografie tematyczne / Cloud Data Warehousing

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  2. Rozprawy doktorskie
  3. Książki
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Gotowa bibliografia na temat „Cloud Data Warehousing”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Cloud Data Warehousing"

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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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Rozprawy doktorskie na temat "Cloud Data Warehousing"

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Attasena, Varunya. "Secret sharing approaches for secure data warehousing and on-line analysis in the cloud." Thesis, Lyon 2, 2015. http://www.theses.fr/2015LYO22014/document.

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Les systèmes d’information décisionnels dans le cloud Computing sont des solutions de plus en plus répandues. En effet, ces dernières offrent des capacités pour l’aide à la décision via l’élasticité des ressources pay-per-use du Cloud. Toutefois, les questions de sécurité des données demeurent une des principales préoccupations notamment lorsqu'il s’agit de traiter des données sensibles de l’entreprise. Beaucoup de questions de sécurité sont soulevées en terme de stockage, de protection, de disponibilité, d'intégrité, de sauvegarde et de récupération des données ainsi que des transferts des do
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Nieva, Gabriel. "Integrating Heterogeneous Data." Thesis, Mittuniversitetet, Avdelningen för arkiv- och datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29950.

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Technological advances, particularly in the areas of processing and storage have made it possible to gather an unprecedented vast and heterogeneous amount of data. The evolution of the internet, particularly Social media, the internet of things, and mobile technology together with new business trends has precipitated us in the age of Big data and add complexity to the integration task. The objective of this study has been to explore the question of data heterogeneity trough the deployment of a systematic literature review methodology. The study surveys the drivers of this data heterogeneity, t
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Maor, Amit. "Using a Data Warehouse as Part of a General Business Process Data Analysis System." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/cmc_theses/1383.

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Data analytics queries often involve aggregating over massive amounts of data, in order to detect trends in the data, make predictions about future data, and make business decisions as a result. As such, it is important that a database management system (DBMS) handling data analytics queries perform well when those queries involve massive amounts of data. A data warehouse is a DBMS which is designed specifically to handle data analytics queries. This thesis describes the data warehouse Amazon Redshift, and how it was used to design a data analysis system for Laserfiche. Laserfiche is a softwar
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Banda, Misheck. "A data management and analytic model for business intelligence applications." Diss., 2017. http://hdl.handle.net/10500/23129.

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Most organisations use several data management and business intelligence solutions which are on-premise and, or cloud-based to manage and analyse their constantly growing business data. Challenges faced by organisations nowadays include, but are not limited to growth limitations, big data, inadequate analytics, computing, and data storage capabilities. Although these organisations are able to generate reports and dashboards for decision-making in most cases, effective use of their business data and an appropriate business intelligence solution could achieve and retain informed decision-making
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Książki na temat "Cloud Data Warehousing"

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H, Tollervey Nicholas, ed. Getting started with Fluidinfo. O'Reilly, 2012.

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Matuszek, Vaughn. Data Cloud : a Solution for Data Warehousing, Data Lakes, Data Engineering, and Data Sharing: Snowflake Data Cloud. Independently Published, 2021.

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Cloud Data Warehousing FD, Snowflake Special Edition (Custom). Wiley & Sons, Incorporated, John, 2016.

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Kraynak, Joseph. Cloud Data Warehousing FD, Snowflake Special Edition (Custom). Wiley & Sons, Incorporated, John, 2016.

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BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud. Apress L. P., 2020.

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Cloud Data Warehousing Volume 1: Architecting Data Warehouse, Lakehouse, Mesh, and Fabric. Technics Publications, LLC, 2023.

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Snowflake Cookbook: Techniques for Building Modern Cloud Data Warehousing Solutions. Packt Publishing, Limited, 2021.

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Cloud Data Warehousing for Dummies, 2nd Snowflake Special Edition (Custom). Wiley & Sons, Incorporated, John, 2019.

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Baum, David, and Joe Kraynak. Cloud Data Warehousing for Dummies, 2nd Snowflake Special Edition (Custom). Wiley & Sons, Incorporated, John, 2019.

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Data intensive storage services for cloud environments. Business Science Reference, 2013.

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Części książek na temat "Cloud Data Warehousing"

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Mucchetti, Mark. "Cloud Shell and Cloud SDK." In BigQuery for Data Warehousing. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6186-6_22.

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Mucchetti, Mark. "Cloud Logging." In BigQuery for Data Warehousing. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6186-6_12.

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Thomsen, Christian, and Torben Bach Pedersen. "Data Warehousing in Cloud Environments." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80623-1.

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Thomsen, Christian, and Torben Bach Pedersen. "Data Warehousing in Cloud Environments." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80623.

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Zohuri, Bahman, and Masoud Moghaddam. "What Is Data Analysis from Data Warehousing Perspective?" In Business Resilience System (BRS): Driven Through Boolean, Fuzzy Logics and Cloud Computation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53417-6_10.

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Lopes, Claudivan Cruz, Valéria Cesário Times, Stan Matwin, Ricardo Rodrigues Ciferri, and Cristina Dutra de Aguiar Ciferri. "Processing OLAP Queries over an Encrypted Data Warehouse Stored in the Cloud." In Data Warehousing and Knowledge Discovery. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10160-6_18.

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Nguyen, Thanh Binh. "Cloud-Based Data Warehousing Application Framework for Modeling Global and Regional Data Management Systems." In Advanced Computational Methods for Knowledge Engineering. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00293-4_24.

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Haval, Abhijeet Madhukar, and Afzal. "Deploying cloud computing and data warehousing to optimize supply chain management and retail analytics." In Applications of Mathematics in Science and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003606659-156.

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d’Orazio, Laurent, and Sandro Bimonte. "Multidimensional Arrays for Warehousing Data on Clouds." In Data Management in Grid and Peer-to-Peer Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15108-8_3.

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Nyunt, Aye Thiri, Brij Kotak, Ravi Chauhan, et al. "Next Generation Data Warehousing for Destination Marketing With Big Data Technologies." In Maximizing Destination Marketing Strategies in the Digital Era. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9939-2.ch006.

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This chapter will discuss evolution of traditional legacy data warehousing into modern big data technology for efficiently managing and analyzing data. Data warehousing brings together structured data from diverse sources on a business. Traditional systems have changed from batch processing to cloud computing and machine learning when handling large unstructured data and real-time information. Big data tools such as Hadoop and Apache Spark make traditional data warehousing faster. Cloud-based data management solutions, such as Amazon Redshift and Google BigQuery, scale and manage cost-effectiv
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Streszczenia konferencji na temat "Cloud Data Warehousing"

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Gholivand, Rezvan, Pejman Goudarzi, and Davood Maleki. "An Improved Hybrid Data Warehousing Architecture for Cloud Service Providers." In 2025 29th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2025. https://doi.org/10.1109/csicc65765.2025.10967465.

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Fernandes, Sérgio, and Jorge Bernardino. "Cloud Data Warehousing for SMEs." In 11th International Conference on Software Engineering and Applications. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0005996502760282.

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Cuzzocrea, Alfredo. "Warehousing and Protecting Big Data." In ICC '16: International Conference on Internet of things and Cloud Computing. ACM, 2016. http://dx.doi.org/10.1145/2896387.2900335.

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Yasser, Farah, and Ayman Alserafi. "Cloud Based Data Warehousing: Challenges and Criteria." In 2023 2nd International Conference on Smart Cities 4.0. IEEE, 2023. http://dx.doi.org/10.1109/smartcities4.056956.2023.10525916.

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Sebaa, Abderrazak, Amina Nouicer, Fatima Chikh, and Abdelkamel Tari. "Big Data Technologies to Improve Medical Data Warehousing." In BDCA'17: 2nd international Conference on Big Data, Cloud and Applications. ACM, 2017. http://dx.doi.org/10.1145/3090354.3090376.

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Hameed, Aqsa, Saqib Ali, Rodger Les Cottrell, and Bebo White. "Applying big data warehousing and visualization techniques on pingER data." In UCC '16: 9th International Conference on Utility and Cloud Computing. ACM, 2016. http://dx.doi.org/10.1145/3006299.3006337.

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Chen, Yi-Ming, and Yan-Hao Chu. "Outsourcing Data Mining Tasksto Cloud While Preserving Customer Privacy." In Annual International Academic Conference on Business Intelligence and Data Warehousing. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-6308-1_59.

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Liu, Zhi, Haowen Wu, Tongxin Bai, Yang Wang, and Chengzhong Xu. "Towards Elastic Data Warehousing by Decoupling Data Management and Computation." In ICCBDC '20: 2020 4th International Conference on Cloud and Big Data Computing. ACM, 2020. http://dx.doi.org/10.1145/3416921.3416935.

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Surya Brahma, Somina Venkata, and Linga Sarma. "Information Management and Accessibility in Business Intelligence in Cloud Vs Conventional Business Intelligence." In Annual International Academic Conference on Business Intelligence and Data Warehousing. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-6308-1_45.

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Sharma, Yashvardhan, Raghib Nasri, and Kumar Askand. "Building a data warehousing infrastructure based on service oriented architecture." In 2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM). IEEE, 2012. http://dx.doi.org/10.1109/iccctam.2012.6488077.

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