# Apache Superset

> Open-source data exploration and visualization at scale

Apache Superset is an open-source BI platform with a rich visualization library, SQL IDE, and fine-grained access control, built for customization and larger deployments.

Website: https://superset.apache.org

## Use it when

- The team needs open-source BI with both a no-code chart builder and a full SQL IDE in one deployment.
- Connector reach matters: eighty-one documented databases over SQLAlchemy, from the warehouses to Druid, Pinot, and Drill.
- Roles and row-level security must exist without a commercial tier.
- Customization is expected: runtime chart plugins, Jinja-templated datasets, and caching layers reward the configuration effort.

## Think twice when

- Nobody wants to operate it: ops complexity is high, and alerts alone need a Celery worker, a scheduler, Redis, and a headless Chromium.
- Non-technical self-serve is the goal; Metabase gets there with far less administration.
- A governed metric layer is required: datasets with metrics are lightweight next to LookML or a dbt-based semantic layer.

## How it runs

Self-hosted Python services, with the Celery stack for schedules and alerts, or managed through Preset and the cloud marketplaces. Queries push down over SQLAlchemy with a configurable cache; dashboards export and import as YAML; embedding works through guest tokens and an SDK once the feature is enabled.

## Details

### General

| Attribute | Value |
| --- | --- |
| License | Open source (permissive) |
| SPDX identifier | Apache-2.0 |
| Deployment | Self-hosted, Managed |
| Workload | Interactive |
| Operational complexity | High |
| Pricing | Free, Subscription |
| Language | Python, TypeScript |
| Repository | https://github.com/apache/superset |
| Documentation | https://superset.apache.org/docs/ |
| Stars | 74.8k |

### Business Intelligence

| Attribute | Value |
| --- | --- |
| Authoring model | No-code Explore chart builder, or SQL Lab |
| Semantic layer | Lightweight: datasets with metrics and calculated columns, defined in the app |
| Connectors | Eighty-one documented databases over SQLAlchemy, the warehouses, plus Druid, Pinot, Kylin, Doris, DataFusion, IoTDB, Solr, Drill, Phoenix and DynamoDB; anything with a Python driver can be added |
| Query execution | Pushdown over SQLAlchemy; a configurable cache eases database load |
| Chart library | A large built-in chart set, now extended by .supx packages loaded at runtime through module federation, with a community registry, no frontend rebuild any more |
| Definitions in | Metadata database; export and import as a ZIP of YAML files |
| Access control | Flask-AppBuilder roles (Admin, Alpha, Gamma and sql_lab) with a permission created per data source, plus row-level security filters that append a clause to the generated WHERE |
| Alerts and delivery | Alerts on a SQL condition and reports on a schedule, to email, Slack or a webhook, but they want a Celery worker, a beat scheduler, Redis and a headless Chromium to screenshot the dashboard first |
| Embedding | Configure a dashboard for embedding, mint a guest token from the API, and render it with the embedded SDK; the feature is off until enabled |
| Billing unit | Nothing to pay: an Apache project with no paid tier of its own, so the cost is whatever you spend hosting it or buying it from someone else |

## Capabilities

- [Business Intelligence](https://matca.io/capabilities/business-intelligence)

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Source: https://matca.io/apache-superset
Last updated: 2026-08-31T11:40:48.832Z