databendlabs/databend
Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 5 days ago, with 226 contributors on GitHub.
Use Cases & Benefits
- Provides a multimodal cloud data warehouse supporting structured, semi-structured, vector, and geospatial data with Snowflake-compatible SQL.
- Delivers 10x faster query performance and 90% cost reduction using Rust-powered vectorized execution and S3-native storage without vendor lock-in.
- Use for building AI-native data platforms with integrated vector search, embedding generation, and full-text search capabilities.
- Use for running large-scale, real-time analytics on petabyte-scale datasets with high concurrency and low latency.
- Use for querying diverse data lake formats like Parquet, CSV, and Avro directly from S3 in a unified SQL environment.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Multimodal data warehouse for the AI era with Snowflake-compatible SQL
Multimodal Data Warehouse: Analyze structured, semi-structured, vector, and geospatial data with unified Snowflake-compatible SQL.
AI-Native Platform: Built-in vector search, AI functions, embedding generation, and full-text search - no separate systems needed.
10x Faster & 90% Cost Reduction: Rust-powered vectorized execution with S3-native storage eliminates vendor lock-in and proprietary overhead.
Deploy Anywhere, Connect Everything: 100% open source - run locally with
pip install databend, self-host, or use managed cloud clusters. All instances share the same data seamlessly.Production Proven: Trusted by world-class enterprises managing 800+ petabytes and 100+ million queries daily.
Enterprise Ready: Fine-grained access control, data masking, and audit logging with complete data sovereignty.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
901+9
Open issues
498-50
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| D4Vinci/Scrapling | 101.1K | +6.3K | 75 | 6 days ago |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| heygen-com/hyperframes | 82.3K | +4.0K | 75 | today |
| harry0703/MoneyPrinterTurbo | 142.0K | +3.8K | 75 | 3 days ago |
| github/spec-kit | 149.2K | +2.6K | 75 | today |
| databendlabs/databend | 9.5K | — | 60 | 5 days ago |
| Category median | 115.8K | +4.0K | 75 | — |
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