Apache Superset vs DataLens
| Tagline | Enterprise-ready BI web app for data exploration and dashboards | Yandex's open-source BI and data visualization system |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Looker, Power BI | Tableau, Power BI, Looker |
| GitHub stars | 75k | 1.7k |
| Language | TypeScript | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Self-host difficulty | 3/5 Moderate | 3/5 Moderate |
| Deploy options | Docker Docker Compose Kubernetes Manual | Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | yesterday | 22 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
DataLens
- Ships with a limited set of connectors (ClickHouse, PostgreSQL) compared to commercial BI
- Documentation and community are smaller and partly Russian-language oriented
- Multi-service architecture makes self-hosting more involved than lightweight alternatives
- Fewer advanced governance, modeling, and enterprise integrations than Tableau/Power BI
Bottom line
Both are a similar lift to self-host; 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.