databendlabs/databend
Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3. observed · 2026-08-28
Health v2 · maintenance only
93/100
- Activity 99
- Release rhythm 80
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 19.0
- age_days: 2153
- days_rel: 138
- days_push: 7
- n_releases_24m: 27
Adoption not part of the score
9423 stars · 896 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Databend is an open-source, cloud-native data warehouse built in Rust that runs entirely on object storage (S3, Azure, GCS). It unifies BI analytics, vector search, full-text search, and geospatial analysis in one engine with Snowflake-compatible SQL, plus sandboxed Python UDFs for AI agent orchestration.
Use cases
- run large-scale OLAP analytics on data in S3
- store and query vector embeddings for semantic search in SQL
- replace Snowflake with a self-hosted open-source warehouse
- build AI agents that query enterprise data via sandboxed Python UDFs
- full-text and hybrid search over structured and unstructured data
- geospatial queries and location-based services
- streaming ingestion and real-time dashboards with Kafka and BI tools
When to choose
- you want a Snowflake-compatible warehouse on your own object storage
- you need analytics, vector search, and full-text search in one engine
- you are building agent/AI workloads that need SQL access to enterprise data
- you want elastic, cloud-native scaling with separation of compute and storage
When to avoid
- you need a small embedded or single-node OLTP database
- your workload is primarily transactional rather than analytical
- you require a strict OSI license — the license is custom (NOASSERTION)
- you need mature ecosystem parity with Snowflake or BigQuery
Facets
service · maturity active
database vector-database search-engine analytics etl rag mcp geospatial databases big-data analytics artificial-intelligence self-hosted cloud-computing self-hosted cloud rust python go jvm data-warehouse olap lakehouse snowflake-compatible object-storage serverless sql udf-sandbox ai-agents full-text-search vector-search search data-engineering linux macos docker nodejs
6 sources
- readme: https://github.com/databendlabs/databend · fetched 2026-08-28 · afe31f1197b6
- homepage: https://docs.databend.com · fetched 2026-08-29 · 6d13de819387
- site_page: https://docs.databend.com/integrations · fetched 2026-08-29 · 62412b8d58ef
- site_page: https://docs.databend.com/release-notes · fetched 2026-08-29 · aefdcc89f2cb
- site_page: https://docs.databend.com/guides/self-hosted/quickstart · fetched 2026-08-29 · 5e28b972a6e2
- site_page: https://docs.databend.com/ · fetched 2026-08-29 · 6d13de819387
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| databendlabs/databend | main | 93 |
For agents
markdown · JSON · MCP: product_card(name="databendlabs/databend")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem