# An End to End Edge to Cloud Data and Analytics Strategy

arXiv: https://arxiv.org/abs/2509.12296 · HTML: https://mlsanity.com/paper/2509.12296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Challenges and Opportunities in Edge Computing](https://mlsanity.com/paper/1609.01967/) (2016)
- [On data lake architectures and metadata management](https://mlsanity.com/paper/2107.11152/) (2021)
- [Cloud Migration Process A Survey Evaluation Framework and Open Challenges](https://mlsanity.com/paper/2004.10725/) (2020)

## Abstract (arXiv)

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.