# databendlabs/databend

Data Agent Ready Warehouse : One for  Analytics, Search, AI, Python Sandbox.  — rebuilt from scratch. Unified architecture on your S3.

Repository: https://github.com/databendlabs/databend
Canonical: https://ross.abutalabs.com/products/databend
Homepage: https://docs.databend.com
Language: Rust
License: NOASSERTION
License Family: other
Topics: rust, database, serverless, bigdata, snowflake, ai, lakehouse, olap, sql, vector-database, cloud-native, elasticsearch, geospatial, vector-search
Last push: 2026-08-26T16:49:34+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 80, longevity 100
- inputs: {"age_days": 2153, "days_push": 7, "days_rel": 138, "gap_med": 19.0, "n_releases_24m": 27}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9423, forks 896 (observed 2026-08-28T04:10:31.100113+00:00)

## What it is
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
- artifact type: service
- maturity: active
- function: database, vector-database, search-engine, analytics, etl, rag, mcp, geospatial
- domain: databases, big-data, analytics, artificial-intelligence, self-hosted, cloud-computing
- platform: self-hosted, cloud, rust, python, go, jvm
- tags: 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

## Member repositories
- databendlabs/databend (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:31.100113+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:22:09.523548+00:00, confidence not recorded.
  - readme: https://github.com/databendlabs/databend (fetched 2026-08-28T04:10:31.100113+00:00, sha afe31f1197b6)
  - homepage: https://docs.databend.com (fetched 2026-08-29T08:22:12.072646+00:00, sha 6d13de819387)
  - site_page: https://docs.databend.com/integrations (fetched 2026-08-29T08:22:12.081832+00:00, sha 62412b8d58ef)
  - site_page: https://docs.databend.com/release-notes (fetched 2026-08-29T08:22:12.083832+00:00, sha aefdcc89f2cb)
  - site_page: https://docs.databend.com/guides/self-hosted/quickstart (fetched 2026-08-29T08:22:12.085793+00:00, sha 5e28b972a6e2)
  - site_page: https://docs.databend.com/ (fetched 2026-08-29T08:22:12.087676+00:00, sha 6d13de819387)
- Data as of 2026-08-30T08:39:29.467469+00:00.