An End to End Edge to Cloud Data and Analytics Strategy
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911158135422976 |
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| author | Butte, Vijay Kumar Butte, Sujata |
| author_facet | Butte, Vijay Kumar Butte, Sujata |
| contents | There is an exponential growth of connected Internet of Things (IoT) devices. These have given rise to applications that rely on real time data to make critical decisions quickly. Enterprises today are adopting cloud at a rapid pace. There is a critical need to develop secure and efficient strategy and architectures to best leverage capabilities of cloud and edge assets. This paper provides an end to end secure edge to cloud data and analytics strategy. To enable real life implementation, the paper provides reference architectures for device layer, edge layer and cloud layer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12296 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An End to End Edge to Cloud Data and Analytics Strategy Butte, Vijay Kumar Butte, Sujata Distributed, Parallel, and Cluster Computing Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Software Engineering There is an exponential growth of connected Internet of Things (IoT) devices. These have given rise to applications that rely on real time data to make critical decisions quickly. Enterprises today are adopting cloud at a rapid pace. There is a critical need to develop secure and efficient strategy and architectures to best leverage capabilities of cloud and edge assets. This paper provides an end to end secure edge to cloud data and analytics strategy. To enable real life implementation, the paper provides reference architectures for device layer, edge layer and cloud layer. |
| title | An End to End Edge to Cloud Data and Analytics Strategy |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2509.12296 |