databendlabs/databend

What it solves

Databend provides a unified enterprise data warehouse that combines large-scale analytics, vector search, and full-text search. It specifically addresses the needs of AI agents by providing a secure environment to run agent logic directly on enterprise data without risking production stability.

How it works

Built in Rust, Databend uses a three-layer architecture to support AI workloads:

1. Control Plane: Manages resource scheduling, permissions, and the lifecycle of sandboxes.
2. Execution Plane: Handles SQL orchestration and communicates via Arrow Flight.
3. Compute Plane: Uses isolated Sandbox Workers to run Python User Defined Functions (UDFs) where agent logic (like LLM calls and tool use) is executed.

It also features Git-like data branching, allowing agents to operate on production snapshots for safe experimentation.

Who it’s for

It is designed for enterprises building AI agents, RAG (Retrieval-Augmented Generation) systems, and large-scale analytics and BI applications that require cloud-native scalability (S3/Azure/GCS).

Highlights

• Unified Engine: Combines analytics, vector search, and full-text search in one place.
• Sandbox UDFs: Allows running Python-based agent logic securely within the database.
• Data Branching: Enables safe experimentation on production data through versioning.
• Cloud Native: Supports elastic compute and integrates with major cloud storage providers.