https://github.com/wilfrednjeunwi/kn-eo-workflow
E2E serverless Earth Observation workflow following Cloud-Native principles, build with K8s, Knative, Python, Minio, Redis
https://github.com/wilfrednjeunwi/kn-eo-workflow
cloud-native cloud-optimized-geotiff data-locality faas knative kubernetes minio python redis serverless
Last synced: about 1 year ago
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E2E serverless Earth Observation workflow following Cloud-Native principles, build with K8s, Knative, Python, Minio, Redis
- Host: GitHub
- URL: https://github.com/wilfrednjeunwi/kn-eo-workflow
- Owner: WilfredNjeunwi
- Created: 2025-03-25T20:34:23.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-25T21:19:02.000Z (about 1 year ago)
- Last Synced: 2025-03-25T21:32:05.963Z (about 1 year ago)
- Topics: cloud-native, cloud-optimized-geotiff, data-locality, faas, knative, kubernetes, minio, python, redis, serverless
- Language: Python
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Serverless Earth Observation Workflow with Cloud-Native Principles 🌍🛰️
Welcome to the "kn-eo-workflow" repository, where we explore an end-to-end serverless Earth Observation workflow following Cloud-Native principles. This workflow is built using K8s, Knative, Python, Minio, and Redis, leveraging the power of cloud-native technologies to process Earth Observation data efficiently and effectively.
## Repository Overview
- **Name**: kn-eo-workflow
- **Description**: E2E serverless Earth Observation workflow following Cloud-Native principles, built with K8s, Knative, Python, Minio, Redis
- **Topics**: cloud-native, cloud-optimized-geotiff, data-locality, faas, k8s, knative, kubernetes, minio, python, redis, serverless
## Features
Our repository focuses on creating a streamlined workflow for Earth Observation data processing, utilizing a serverless architecture with cloud-native technologies. By incorporating Kubernetes, Knative, Python, Minio, and Redis, we ensure optimal performance, scalability, and efficiency in handling Earth Observation data across various sources and formats.
## How to Use
To access the latest releases of our Earth Observation workflow, please visit [this link](https://github.com/WilfredNjeunwi/kn-eo-workflow/releases). Explore our releases to access the most up-to-date version of our workflow, featuring enhancements and optimizations for seamless Earth Observation data processing.
## Stay Updated
By checking the "Releases" section regularly, you can stay informed about the latest updates and improvements to our serverless Earth Observation workflow. Be sure to explore the detailed release notes to understand the changes and enhancements made with each new version.
## Get Involved
Join our community of Earth Observation enthusiasts and cloud-native technology experts to contribute, provide feedback, and collaborate on advancing our serverless workflow. By working together, we can enhance the efficiency and capabilities of Earth Observation data processing, benefiting various industries and research domains.
## Future Roadmap
Our team is committed to continuously improving and expanding the capabilities of our serverless Earth Observation workflow. Stay tuned for upcoming features, integrations, and enhancements that will further optimize the processing and utilization of Earth Observation data within a cloud-native environment.
## Conclusion
The "kn-eo-workflow" repository offers a robust and scalable solution for processing Earth Observation data using cloud-native principles and serverless architectures. By leveraging technologies such as Kubernetes, Knative, Python, Minio, and Redis, we provide a flexible and efficient workflow for handling diverse Earth Observation datasets. Join us on this exciting journey as we innovate and optimize Earth Observation data processing in a cloud-native ecosystem.
🛰️📊🌐 Let's optimize Earth Observation workflows with cloud-native technologies! 🌍🔍🚀
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