Apache Superset
Data exploration and BI platform from the Apache Software Foundation: 40+ chart types, SQL Lab and dashboards you host.
Business Intelligence
Apache Superset is a business intelligence and data exploration web application from the Apache Software Foundation, released under Apache-2.0 and free to self-host at any scale; the project itself sells nothing. You install it with Docker Compose, on Kubernetes through Helm, or from PyPI, and point it at a SQL warehouse you already have, where it renders 40+ chart types, dashboards and a SQL editor without copying the data into itself. The trade-offs are documented by the project: its Docker Compose setup is stated not to be supported or recommended for production, a PyPI install gives you only the application because caching, alerts, asynchronous queries and thumbnails need Redis plus Celery workers and a beat scheduler, and the Explore interface cannot join tables because a dataset is a single table or a view. It queries any datastore that has a Python DB-API driver and a SQLAlchemy dialect, and the community reports 8 GB of RAM and 2 vCPUs as adequate for a moderately sized instance.
Key features
- 40+ pre-installed chart types
- No-code chart builder and a web SQL editor
- Semantic layer for custom dimensions, metrics and virtual datasets
- Connects to any SQL datastore with a Python DB-API driver
- Caching layer keeps repeated queries off the data warehouse
- Alerts and reports run on Celery workers with a beat scheduler
- Roles, permissions and authentication from Flask AppBuilder
Self-hosted editions
- Self-hosted (Apache-2.0)Free self-hosted
- LicenceOpen source · Apache-2.0DeploymentSelf-hosted
Limits, notes & source
Self-hosted (Apache-2.0)
- Limits
- The complete platform: all chart types, SQL Lab, dashboards, the semantic layer, alerts and reports, with no seat limit. Caching, alerts and asynchronous queries need Redis and Celery workers that you run yourself.
- Notes
- GitHub's licence API reads the repository LICENSE.txt as spdx_id "Apache-2.0"; the project's own site publishes no price because it has no paid edition — only the self-hosted one exists. Third-party companies host Superset for money, which is their product and not an edition of this one.
Pros & cons
Strengths
Trade-offs
- The project does not support its Docker Compose setup for production use cases. Source
- A PyPI install gives you only the application: caching, alerts and async queries need Redis plus Celery workers and a beat scheduler. Source
- The Explore interface cannot join tables: a dataset is a single table or a view. Source
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- No official Windows support; the project targets Linux hosts and containers. Source
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