Ross ROSS = Recommend OSS · open-source software intelligence for agents

apache/superset

Apache Superset is a Data Visualization and Data Exploration Platform observed · 2026-10-18

github.com/apache/superset · homepage · Python · Apache-2.0 (permissive) observed · 2026-10-18

Health v2 · maintenance only

98/100

  • Activity 100
  • Release rhythm 94
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.

  • gap_med: 5
  • age_days: 4109
  • days_rel: 41
  • days_push: 3
  • n_releases_24m: 46

Full methodology

Adoption not part of the score

75196 stars · 18483 forks observed · 2026-10-18

What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-21, confidence 0.97

Apache Superset is an open-source, enterprise‑ready business intelligence web application. It provides a no‑code chart builder, a powerful SQL editor, and a rich library of visualizations.

Use cases

  • Create interactive dashboards from data stored in SQL databases.
  • Explore datasets with a drag‑and‑drop chart builder without writing code.
  • Run ad‑hoc SQL queries and visualize the results instantly.
  • Build and share BI reports for business analysts and stakeholders.
  • Integrate multiple data sources and provide a unified analytics layer.

When to choose

  • You need a self‑hosted, open‑source BI platform with extensive database support.
  • Your team prefers a web UI for chart building and a SQL IDE for advanced queries.
  • You want a highly extensible system that can be customized via plugins and APIs.
  • You require a lightweight, container‑friendly deployment (Docker/Kubernetes).
  • You need role‑based access control and configurable authentication mechanisms.

When to avoid

  • Your data sources are primarily non‑SQL or require real‑time streaming ingestion.
  • You need heavy in‑application data processing or machine‑learning pipelines.
  • You prefer a fully managed SaaS solution with zero operational overhead.
  • Your organization lacks Python or container expertise for deployment and maintenance.
  • You require out‑of‑the‑box support for complex ETL workflows.

Facets

application · maturity active

web-framework api-framework http-server auth caching database-driver data-visualization data-science windows python open-source dashboard sql-editor no-code extensible analytics self-hosted business-intelligence data-engineering linux macos docker kubernetes web-server

10 sources

Member repositories

RepositoryRoleHealth v2
apache/supersetmain98

For agents

markdown · JSON · MCP: product_card(name="apache/superset")

Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem