Table 1
Comparison of Helio-Lite and HelioCloud features.
| FEATURE | HELIO-LITE | HELIOCLOUD |
|---|---|---|
| Deployment Model | Single AWS instance; user-managed JupyterHub | Institutional scale cloud platform managed centrally |
| Target Users | Individual researchers, educators, small groups | Multi-institutional research collaborations |
| Storage Model | Local EBS or optional S3; no persistent backend required | Centralized object storage and mission archives |
| Data Scope | User-specified datasets and Application Program Interfaces (API) (e.g., JSOC, DONKI, DMLab) | Integrated petabyte-scale mission datasets |
| Environment Configuration | Two prebuilt Conda environments (AI/ML, PyHC) | Pre-integrated PyHC software stack with additional HPC modules |
| Use Case Emphasis | Reproducible research, education, and prototyping | Long-term data hosting, HPC workloads, and collaborative analysis |
Figure 1
Overall AWS data migration workflow for Helio-Lite, showing how AIA, HMI, DONKI, and DMLab datasets are accessed and moved into the cloud environment. Users connect through JupyterHub to analyze these datasets without local hardware dependencies.
Figure 2
Helio-Lite login page as seen after deployment.