Databend

Databend is an open-source, self-hosted cloud-native data warehouse designed for large-scale analytics, vector search, and full-text search. The software is deployed as a server and can be run via Docker or as a managed cloud service, providing a foundation for organizations to manage massive datasets without relying on proprietary cloud lock-in.

The system functions as a lakehouse that integrates relational data with AI-driven workloads. It is used for business intelligence, retrieval-augmented generation (RAG), and the orchestration of AI agents. Users interact with the warehouse using SQL and can deploy the system locally for development using a Python driver, allowing for a transition from local testing to enterprise-scale production environments.

Key features

• Vector search and full-text search capabilities
• Sandbox Python User Defined Functions (UDFs) for agent logic
• Git-like data branching for experimentation on production snapshots
• Automatic schema evolution
• Support for S3, Azure, and GCS storage
• Transactional reliability for data operations
• Elastic compute scaling for enterprise workloads
• SQL-based orchestration for AI agents

Built in Rust, the architecture separates the control plane, execution plane, and compute plane to ensure scalability and isolation. The control plane manages resource scheduling and permission validation, while the execution plane handles SQL orchestration and communicates with isolated sandbox workers via Arrow Flight. This design allows developers to run complex agent logic, LLM calls, and reasoning tools directly within the database environment while maintaining security through isolated sandboxes. The system is specifically intended for data engineers and AI developers who require a unified engine for both traditional analytics and modern vector database functionality.

By combining these capabilities, the software eliminates the need to maintain separate databases for structured analytics and unstructured vector search. It provides a cohesive environment where AI agents can operate on production data snapshots safely through its branching mechanism. Databend is an agent-ready data warehouse that bridges the gap between traditional OLAP capabilities and the requirements of enterprise AI workloads.