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databend

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

Description

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

README

Databend

Enterprise Data Warehouse for AI Agents

Large-scale analytics, vector search, full-text search β€” with flexible agent orchestration and secure Python UDF sandboxes. Built for enterprise AI workloads.

☁️ Try Cloud β€’ πŸš€ Quick Start β€’ πŸ“– Documentation β€’ πŸ’¬ Slack



CI Status Platformdatabend

πŸ’‘ Why Databend?

Databend is an open-source enterprise data warehouse built in Rust.

Core capabilities: Analytics, vector search, full-text search, auto schema evolution β€” unified in one engine.

Agent-ready: Sandbox UDFs for agent logic, SQL for orchestration, transactions for reliability, branching for safe experimentation on production data.

πŸ“Š Core Engine
Analytics, vector search, full-text search, auto schema evolution, transactions.
πŸ€– Agent-Ready
Sandbox UDF + SQL orchestration. Build and run agents on your enterprise data.
🏒 Enterprise Scale
Elastic compute, cloud native. S3/Azure/GCS.
🌿 Branching
Git-like data versioning. Agents safely operate on production snapshots.

Databend Architecture

⚑ Quick Start

1. Cloud (Recommended)

Start for free on Databend Cloud β€” Production-ready in 60 seconds.

2. Local (Python)

Ideal for development and testing:

pip install databend
import databend
ctx = databend.SessionContext()
ctx.sql("SELECT 'Hello, Databend!'").show()

3. Docker

Run the full warehouse locally:

docker run -p 8000:8000 datafuselabs/databend

πŸ€– Agent-Ready Architecture

Databend's Sandbox UDF enables flexible agent orchestration with a three-layer architecture:

  • Control Plane: Resource scheduling, permission validation, sandbox lifecycle management
  • Execution Plane (Databend): SQL orchestration, issues requests via Arrow Flight
  • Compute Plane (Sandbox Workers): Isolated sandboxes running your agent logic
-- Define your agent logic
CREATE FUNCTION my_agent(input STRING) RETURNS STRING
LANGUAGE python HANDLER = 'run'
AS $$
def run(input):
    # Your agent logic: LLM calls, tool use, reasoning...
    return response
$$;

-- Orchestrate agents with SQL
SELECT my_agent(question) FROM tasks;

πŸš€ Use Cases

  • AI Agents: Sandbox UDF + SQL orchestration + branching for safe operations
  • Analytics & BI: Large-scale SQL analytics β€” Learn more
  • Search & RAG: Vector + full-text search β€” Learn more

🀝 Community & Support

Contributors are immortalized in the system.contributors table πŸ†

πŸ“„ License

Apache 2.0 + Elastic 2.0 | Licensing FAQ


Enterprise warehouse, agent ready
🌐 Website β€’ 🐦 Twitter

Release History

VersionChangesUrgencyDate
v1.2.881<!-- Release notes generated using configuration in .github/release.yml at ca29960f5cec21270ccd6b964ca02f0d2f33c899 --> ## What's Changed ### Exciting New Features ✨ * feat(query): support create iceberg table with partition and properties by @TCeason in https://github.com/databendlabs/databend/pull/17812 * feat(query): support alter table/database refresh cache by @TCeason in https://github.com/databendlabs/databend/pull/17841 * feat(query): variant support extension types(Decimal, BinarHigh4/17/2026
v1.2.881-nightly<!-- Release notes generated using configuration in .github/release.yml at 1cc88fbd2dd02cfe4760dfa94129fad6626ee42c --> ## What's Changed ### Thoughtful Bug Fix πŸ”§ * fix(query): data lost in new hash join caused by spill by @dqhl76 in https://github.com/databendlabs/databend/pull/19415 ### Code Refactor πŸŽ‰ * refactor(management): simplify UserApi using upsert_pb/get_pb patterns by @TCeason in https://github.com/databendlabs/databend/pull/19413 **Full Changelog**: https://github.com/dLow2/9/2026

Dependencies & License Audit

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