Relevant bibliographies by topics / Snowflake cloud data platforms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Snowflake cloud data platforms'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Snowflake cloud data platforms"

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Karanam, Srinivasa Rao. "Advanced Analytics with Snowflake and Power BI." International Scientific Journal of Engineering and Management 03, no. 09 (2024): 1–7. https://doi.org/10.55041/isjem02074.

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The demand for advanced analytics and near real-time insights has intensified across industries. Organizations are increasingly turning to modern data platforms and powerful business intelligence tools to streamline their analytical processes and drive decision-making. Snowflake has cemented its position as a leading cloud data platform that enables elastic compute scaling, secure data sharing, and powerful governance features. Simultaneously, Microsoft’s Power BI continues to evolve as one of the most widely adopted business intelligence and analytics solutions. The synergy between Snowflake
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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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Prasad, M. Seetharama. "A Comparative Study of Snowflake and SAP BW for Data Analytics." International Journal of Technology, Management and Humanities 11, no. 02 (2025): 1–8. https://doi.org/10.21590/ijtmh.11.02.07.

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The modern data environment demands strong solutions for managing, storing, and analyzing large amounts of data efficiently. Snowflake and SAP Business Warehouse (SAP BW) are two leading platforms in the area of data warehousing and analytics that present different capabilities to handle diverse enterprise needs. The comparative analysis between Snowflake and SAP BW is presented in this study, detailing their architecture, scalability, performance, integration capabilities, and cost-effectiveness. Snowflake is a cloud-native data platform designed for elasticity and scalability, with pay-as-yo
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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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Researcher. "REVOLUTIONIZING DATA MANAGEMENT: KEY INNOVATIONS IN TERADATA AND SNOWFLAKE TECHNOLOGIES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 587–94. https://doi.org/10.5281/zenodo.13347929.

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This article comprehensively examines recent advancements in data engineering, focusing on two leading platforms: Teradata and Snowflake. Through a rigorous methodology encompassing literature review, case study analysis, and industry report examination, the study offers an in-depth exploration of cutting-edge developments in machine learning integration, hybrid cloud solutions, serverless computing, and advanced data governance.&nbsp;The article highlights Teradata's strides in predictive analytics, real-time decision-making capabilities, and performance optimization while showcasing Snowflak
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Dr.A.Shaji, George. "Deciphering the Path to Cost Efficiency and Sustainability in the Snowflake Environment." Partners Universal International Innovation Journal (PUIIJ) 01, no. 04 (2023): 231–50. https://doi.org/10.5281/zenodo.8282654.

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As adoption of the Snowflake cloud data platform continues to accelerate, organizations are seeking ways to optimize costs and resource utilization amidst Snowflake&#39;s unique architecture. This paper examines best practices and considerations for attaining efficiency, managing expenses, and upholding sustainability initiatives within Snowflake implementations. The study begins by providing background on Snowflake&#39;s novel cloud-native architecture, which separates storage from computing. This elasticity enables immense scalability, but can also lead to cost overruns if workloads and reso
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Kukkuhalli, Shreesha Hegde. "Optimizing Snowflake Enterprise Data Platform Cost Through Predictive Analytics and Query Performance Optimization." Journal of Artificial Intelligence & Cloud Computing 3, no. 6 (2024): 1–3. https://doi.org/10.47363/jaicc/2024(3)406.

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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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Researcher. "BUILDING SCALABLE DATA ARCHITECTURES WITH INFORMATICA IICS AND SNOWFLAKE." International Journal of Information Technology and Management Information Systems (IJITMIS) 15, no. 2 (2024): 85–93. https://doi.org/10.5281/zenodo.14498317.

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Modern organisations generate vast and ever-changing amounts of data, requiring optimised, flexible, and expandable data handling environments. This research paper seeks to assess the compatibility of IICS and Snowflake in complex integration, processing, and analytical cloud structures that address today&rsquo;s data challenges. To be more specific, employing those integrated platforms facilitates attaining novel paradigms of scale, speed, and expenses for organisational data management gestures.
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Karanam, Srinivasa Rao. "Revefi for Snowflake Operations." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–8. https://doi.org/10.55041/ijsrem43472.

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Snowflake has emerged as one of the leading cloud data warehouse platforms, offering near-infinite scalability, on-demand compute, and separation of storage and compute resources. While these characteristics facilitate a wide range of data-driven use cases, enterprises often face challenges around costs, performance, and data quality. Revefi, an emerging Data Operations Cloud platform, addresses these core challenges through AI-driven insights, rapid setup, and a metadata-focused architecture designed to bolster security. This technical exploration delves into the key aspects of Revefi for Sno
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Dissertations / Theses on the topic "Snowflake cloud data platforms"

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Vijayakumar, Sruthi. "Hadoop Based Data Intensive Computation on IAAS Cloud Platforms." UNF Digital Commons, 2015. http://digitalcommons.unf.edu/etd/567.

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Cloud computing is a relatively new form of computing which uses virtualized resources. It is dynamically scalable and is often provided as pay for use service over the Internet or Intranet or both. With increasing demand for data storage in the cloud, the study of data-intensive applications is becoming a primary focus. Data intensive applications are those which involve high CPU usage, processing large volumes of data typically in size of hundreds of gigabytes, terabytes or petabytes. The research in this thesis is focused on the Amazon’s Elastic Cloud Compute (EC2) and Amazon Elastic Map Re
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Duarte, Coscia Bruno Marcel. "Evaluation of Network-Layer Security Technologies for Cloud Platforms." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290293.

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With the emergence of cloud-native applications, the need to secure networks and services creates new requirements concerning automation, manageability, and scalability across data centers. Several solutions have been developed to overcome the limitations of the conventional and well established IPsec suite as a secure tunneling solution. One strategy to meet these new requirements has been the design of software-based overlay networks. In this thesis, we assess the deployment of a traditional IPsec VPN solution against a new secure overlay mesh network called Nebula. We conduct a case study b
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Domingos, João Nuno Silva Tabar. "On the cloud deployment of a session abstraction for service/data aggregation." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/9923.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>The global cyber-infrastructure comprehends a growing number of resources, spanning over several abstraction layers. These resources, which can include wireless sensor devices or mobile networks, share common requirements such as richer inter-connection capabilities and increasing data consumption demands. Additionally, the service model is now widely spread, supporting the development and execution of distributed applications. In this context, new challenges are emerging around the “big data” topic. These challenges i
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Muthiah, Karthika Ms. "Performance Evaluation of Hadoop based Big Data Applications with HiBench Benchmarking tool on IaaS Cloud Platforms." UNF Digital Commons, 2017. https://digitalcommons.unf.edu/etd/771.

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Cloud computing is a computing paradigm where large numbers of devices are connected through networks that provide a dynamically scalable infrastructure for applications, data and storage. Currently, many businesses, from small scale to big companies and industries, are changing their operations to utilize cloud services because cloud platforms could increase company’s growth through process efficiency and reduction in information technology spending [Coles16]. Companies are relying on cloud platforms like Amazon Web Services, Google Compute Engine, and Microsoft Azure, etc., for their busines
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Weerasinghe, Jagath [Verfasser], Andreas [Akademischer Betreuer] Herkersdorf, Christian [Gutachter] Plessl, and Andreas [Gutachter] Herkersdorf. "Standalone Disaggregated Reconfigurable Computing Platforms in Cloud Data Centers / Jagath Weerasinghe ; Gutachter: Christian Plessl, Andreas Herkersdorf ; Betreuer: Andreas Herkersdorf." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1160381321/34.

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Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.

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This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies
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Shamon, Olivia, and Lucas Carlberg. "iipax one as a Service in Cloud Integration Platforms : A Comparison of Zapier, IFTTT and Power Automate." Thesis, Linköpings universitet, Tekniska fakulteten, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165631.

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This thesis covers the theme of cloud integration platforms as a service with Ida Infronts information and task management system iipax one. The premise of this thesis is to research the most optimal cloud integration platform and find what cloud-based functions that are useful for iipax one. Using a cloud integration platform with iipax one could be beneficial for the users as it allows them to automate simple repetitive tasks. But choosing an integration platform is not a simple undertaking as there are many available platforms, all with their own unique features. To find a suitable platform
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Carpen-Amarie, Alexandra. "BlobSeer as a data-storage facility for clouds : self-Adaptation, integration, evaluation." Thesis, Cachan, Ecole normale supérieure, 2011. http://www.theses.fr/2011DENS0066/document.

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L’émergence de l’informatique dans les nuages met en avant de nombreux défis qui pourraient limiter l’adoption du paradigme Cloud. Tandis que la taille des données traitées par les applications Cloud augmente exponentiellement, un défi majeur porte sur la conception de solutions efficaces pour la gestion de données. Cette thèse a pour but de concevoir des mécanismes d’auto-adaptation pour des systèmes de gestion de données, afin qu’ils puissent répondre aux exigences des services de stockage Cloud en termes de passage à l’échelle, disponibilité et sécurité des données. De plus, nous nous propo
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Moise, Diana Maria. "Optimizing data management for MapReduce applications on large-scale distributed infrastructures." Thesis, Cachan, Ecole normale supérieure, 2011. http://www.theses.fr/2011DENS0067/document.

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Les applications data-intensive sont largement utilisées au sein de domaines diverses dans le but d'extraire et de traiter des informations, de concevoir des systèmes complexes, d'effectuer des simulations de modèles réels, etc. Ces applications posent des défis complexes tant en termes de stockage que de calcul. Dans le contexte des applications data-intensive, nous nous concentrons sur le paradigme MapReduce et ses mises en oeuvre. Introduite par Google, l'abstraction MapReduce a révolutionné la communauté intensif de données et s'est rapidement étendue à diverses domaines de recherche et de
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"Access-pattern-aware data management in cloud platforms." 2015. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291497.

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Database outsourcing is an emerging paradigm for data management in which data are stored in third-party servers. With the advance of cloud computing, database outsourcing has become popular and highly adopted. However, as a result, many technology challenges have arisen.<br>In this thesis, we study two problems with respect to the challenges, and propose solutions for each problem with the consideration of access patterns. The first problem is raised from theviewpoint of service providers. We study the problem of data allocation in scalable distributed database systems for achieving the high
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Books on the topic "Snowflake cloud data platforms"

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Carruthers, Andrew. Building the Snowflake Data Cloud. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8593-0.

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Carruthers, Andrew. Tuning the Snowflake Data Cloud. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0379-6.

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Carruthers, Andrew, and Sahir Ahmed. Maturing the Snowflake Data Cloud. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9340-9.

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Tuning the Snowflake Data Cloud: Performance Tuning Your Snowflake Data Platform. Apress L. P., 2024.

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Zburivsky, Danil, and Lynda Partner. Designing Cloud Data Platforms. Manning Publications Co. LLC, 2021.

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Zburivsky, Danil, and Lynda Partner. Designing Cloud Data Platforms. Manning Publications Co. LLC, 2021.

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Snowflake Security: Securing Your Snowflake Data Cloud. Apress L. P., 2022.

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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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Carruthers, Andrew. Building the Snowflake Data Cloud: Monetizing and Democratizing Your Data. Apress L. P., 2022.

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

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Book chapters on the topic "Snowflake cloud data platforms"

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Ferle, Maja. "Introduction to the Snowflake Cloud Data Platform." In SnowPro™ Core Certification Companion. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9078-1_2.

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Bell, Frank, Raj Chirumamilla, Bhaskar B. Joshi, Bjorn Lindstrom, Ruchi Soni, and Sameer Videkar. "The Snowflake Data Cloud." In Snowflake Essentials. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7316-6_1.

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Bell, Frank, Raj Chirumamilla, Bhaskar B. Joshi, Bjorn Lindstrom, Ruchi Soni, and Sameer Videkar. "Snowflake Data Cloud Architecture." In Snowflake Essentials. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7316-6_3.

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Morton, Adam. "Data Sharing and the Data Cloud." In Mastering Snowflake Solutions. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8029-4_7.

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Carruthers, Andrew. "The Snowflake Data Cloud." In Building the Snowflake Data Cloud. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8593-0_1.

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Steelman, Ronald. "The Snowflake Data Cloud." In Apress Pocket Guides. Apress, 2024. https://doi.org/10.1007/979-8-8688-0382-6_2.

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Carruthers, Andrew, and Sahir Ahmed. "Data Catalog." In Maturing the Snowflake Data Cloud. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9340-9_7.

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Carruthers, Andrew. "Data Presentation." In Building the Snowflake Data Cloud. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8593-0_9.

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Carruthers, Andrew. "Data Pipelines." In Building the Snowflake Data Cloud. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8593-0_8.

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Carruthers, Andrew. "Data Modeling." In Building the Snowflake Data Cloud. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8593-0_13.

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Conference papers on the topic "Snowflake cloud data platforms"

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Nageshwaran, Vinoth. "Novartis’ Strategic Adoption of Snowflake Data Cloud Transforms Data Management and Drives Breakthroughs in Drug Discovery." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760589.

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Meehan, Kevin, Francis McDermott, and Naomi Petropoulos. "Evaluating Automatic Transcription Models Utilising Cloud Platforms." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800465.

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Aladiyan, Anbarasu. "Efficient Data Structures and Algorithms for Cloud Computing Platforms." In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2024. http://dx.doi.org/10.1109/icacite60783.2024.10617203.

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Gayathri, S., Devaraj S, Muthupandi B, and Vishwaraj S. "Healthcare Data Protection and Biometric Access on Cloud Platforms." In 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). IEEE, 2025. https://doi.org/10.1109/icmcsi64620.2025.10883394.

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Zhang, Zhehang, Bharadwaj Madabhushi, Sandip Kundu, and Russell Tessier. "Security Risks Due to Data Persistence in Cloud FPGA Platforms." In 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2024. http://dx.doi.org/10.1109/mwscas60917.2024.10658894.

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Xiaoyu, Yin, Zhang Min, Yue Zhongwen, Song Yuhang, Li Shaochong, and Tao Jun. "Secure Data Disposal Algorithm for Edge Computing-Enhanced Cloud Platforms." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10868667.

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Thangalakshmi, S., N. Mohankumar, L. N. Jayanthi, Y. M. Blessy, R. Meenakshi, and M. Rajmohan. "Telemedicine Platforms with Cloud-Based AI-Driven Diagnostics in Hospitals." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961133.

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Nagarkar, Ranjeet, Colin Bennie, Kejia Wang, Mark Lam, Daniel Gonzalez, and Mahesh B. Chaudhari. "Integrating Multiple Cloud Platforms to Build a Data Pipeline for Recommendation Systems." In 2024 7th International Conference on Data Science and Information Technology (DSIT). IEEE, 2024. https://doi.org/10.1109/dsit61374.2024.10881634.

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Abusalih Rahumath Ali, AbuFaizur Rahman, K. Saravanan, B. Bala Abirami, V. Samuthira Pandi, Vijay Vasanth Aroulanandam, and S. Parthiban. "Enhancing Cloud Security with AI: Developing Robust, Scalable Solutions for Threat Mitigation and Data Protection in Cloud Platforms." In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10863942.

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Vennala, B. L., Devanolla Suresh, P. Samiya, M. Suresh, and B. Vamsi. "Integrating Artificial Intelligence with Cloud Platforms to Optimize Performance, Scalability, and Reliability in Distributed Computing Systems." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011769.

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Reports on the topic "Snowflake cloud data platforms"

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Kholoshyn, Ihor V., Olga V. Bondarenko, Olena V. Hanchuk, and Iryna M. Varfolomyeyeva. Cloud technologies as a tool of creating Earth Remote Sensing educational resources. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3885.

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This article is dedicated to the Earth Remote Sensing (ERS), which the authors believe is a great way to teach geography and allows forming an idea of the actual geographic features and phenomena. One of the major problems that now constrains the active introduction of remote sensing data in the educational process is the low availability of training aerospace pictures, which meet didactic requirements. The article analyzes the main sources of ERS as a basis for educational resources formation with aerospace images: paper, various individual sources (personal stations receiving satellite infor
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Malone, Matt. Will Canada’s Bill C-8 Impact the Future of EU-Canada Cross-border Data Flows? Balsillie School of International Affairs, 2025. https://doi.org/10.51644/bap74.

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Canada’s proposed cyber security for critical infrastructure bill, Bill C-8, which reintroduces Bill C-26 from the previous Parliament (with minor tweaks), could lead to a re-evaluation of the European Commission’s adequacy decision concerning Canada’s data protection laws. As such, the bill could push Canada to the brink of a disruption in the framework governing cross-border data flows between the two jurisdictions. Such a scenario would create tremendous uncertainty for actors in the digital economy who routinely process Europeans’ personal data, such as airlines, financial institutions, in
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McHugh, Power, and Randell. L51972 Encroachment Monitoring via Earth Observation Data. Pipeline Research Council International, Inc. (PRCI), 2001. http://dx.doi.org/10.55274/r0011240.

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The objective of the pipeline encroachment monitoring project was to demonstrate the feasibility of using Earth observation (EO) satellites for early detection of third-party encroachment activities. Examples of encroachment activities include road construction, cable laying, farming, and residential or commercial development. Optical and synthetic aperture radar (SAR) images taken from space-borne platforms were evaluated for use in detecting encroachment activities. C-CORE and TransCanada Pipelines Ltd. conducted an encroachment field program near Whitecourt, Alberta. Various encroachment ac
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Papadakis, Stamatios, Арнольд Юхимович Ків, Hennadiy M. Kravtsov, et al. Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7375.

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The article presents the proceedings of two workshops: Cloud-based Smart Technologies for Open Education Workshop (CSTOE 2022) and Illia O. Teplytskyi Workshop on Computer Simulation in Education (CoSinE 2022) held in Kyiv, Ukraine, on December 22, 2022. The CoSinE workshop focuses on computer simulation in education, including topics such as computer simulation in STEM education, AI in education, and modeling systems in education. The CSTOE workshop deals with cloud-based learning resources, platforms, and infrastructures, with topics including personalized learning and research environment d
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Solovyanenko, Nina I. ЮРИДИЧЕСКИЕ СТРАТЕГИИ ЦИФРОВОЙ ТРАНСФОРМАЦИИ АГРАРНОГО БИЗНЕСА. DOI CODE, 2021. http://dx.doi.org/10.18411/0131-5226-2021-70004.

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t.The development of global agricultural production and food trade in recent decades implies a digital transformation and the transition to a new technological order, which is an essential factor for sustainable development. Digitalization of agriculture and the food sector is carried out on the basis of IT 2 platforms, the Internet of Things, cloud computing, big data, artificial intelligence, and blockchain technology. Fragmented and unclear legal mechanisms, slow updating of legal regulation hinder the introduction of digital solutions. A modern regulatory framework based on digital strateg
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Solovyanenko, Nina I. Legal features of innovative (digital) entrepreneurship in the agricultural and food sector. DOI CODE, 2021. http://dx.doi.org/10.18411/0131-5226-2021-70008.

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Modern agricultural production and food trade are involved in the process of digital transformation, which is a cardinal factor of sustainable development and is carried out on the basis of IT platforms, the Internet of Things, cloud computing, big data, artificial intelligence, blockchain technologies. The COVID-19 pandemic has increased the dependence of these sectors of the economy on information and communication technology infrastructure and services. At the same time, the slow updating of legislation, which lags behind the constantly improving digital technologies, not only hinders their
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Kaleagasi, Bartu, Sean McCarthy, and Peter Beaumont. Geospatial Public Policy: Global Best Practices for Harnessing the Potential of Satellite Technologies and Applications. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004484.

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This publication presents six case studies of public policies that promote the development and use of geospatial technologies and applications, which can be categorized in five layers: institutions, tools, data, skills and industry. The evolution of these technologies and applications over the past decade has been driven by the understanding that where people and things are located is central to smart decision making. As a result of low-cost launch vehicles, increasing numbers of satellites in orbit, new sensor technologies, machine learning algorithms, advances in cloud computing, and the eme
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Shyshkina, Mariya P., and Maiia V. Marienko. Augmented reality as a tool for open science platform by research collaboration in virtual teams. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3755.

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The provision of open science is defined as a general policy aimed at overcoming the barriers that hinder the implementation of the European Research Area (ERA). An open science foundation seeks to capture all the elements needed for the functioning of ERA: research data, scientific instruments, ICT services (connections, calculations, platforms, and specific studies such as portals). Managing shared resources for the community of scholars maximizes the benefits to society. In the field of digital infrastructure, this has already demonstrated great benefits. It is expected that applying this p
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