Apache Superset vs Dashy
| Tagline | Enterprise-ready BI web app for data exploration and dashboards | Feature-rich homelab homepage with easy YAML configuration and a polished UI |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Looker, Power BI | Tableau, Looker, Power BI |
| GitHub stars | 73k | 25k |
| Language | TypeScript | Nodejs |
| License | Apache-2.0 | MIT |
| Self-host difficulty | 3/5 Moderate | 2/5 Easy |
| Deploy options | Docker Docker Compose Kubernetes Manual | Docker Docker Compose Manual |
| Managed hosting | ||
| Last updated | today | 2 days ago |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Apache Superset
- No native desktop authoring app like Tableau Desktop; all work happens in the browser
- Visualization customization is less polished and flexible than Tableau's drag-and-drop canvas
- No built-in semantic/modeling layer comparable to Looker's LookML (relies on external tools)
- Steeper learning curve and heavier infrastructure (Celery, Redis, metadata DB) for production
Dashy
- No analytical data visualization, BI queries, or database connectivity
- Multi-user support is basic; no proper RBAC or team workspaces
- Service auto-discovery requires manual YAML entries; no Docker auto-detection like Homepage
- Not suitable for business reporting or data-driven dashboards
Bottom line
Choose Dashy if you want the lower-effort setup; choose Apache Superset for the larger community and ecosystem. Apache Superset has seen more recent development. Open each guide below for deploy steps and the full feature gap.
Dashy
Feature-rich homelab homepage with easy YAML configuration and a polished UI