Databend is an open-source, cloud-native data warehouse rebuilt from scratch in Rust, designed for enterprise AI workloads. It unifies large-scale SQL analytics, vector search, full-text search, and auto schema evolution in a single engine, enabling AI agents to safely operate on production data using SQL orchestration and isolated Python UDF sandboxes. Built for S3, Azure Blob, and GCS, it supports elastic compute and Git-like data branching for safe experimentation.
Databend integrates deeply with modern data stacks through native SQL clients, language drivers (Python, Go, Java, Node.js, Rust), BI tools like Metabase and Superset, ELT platforms like Airbyte and dbt, and AI frameworks like MindsDB and MCP Server. It deploys as a cloud service, Docker container, or local Python library, making it flexible for both production and development environments.
What You Get
- Sandbox UDFs for AI Agents - Run isolated Python code within SQL queries to implement LLM reasoning, tool use, and agent logic directly on enterprise data.
- Vector Search - Native support for vector embeddings and similarity search, enabling RAG pipelines without external vector databases.
- Full-Text Search - Built-in full-text search capabilities for unstructured text data alongside structured SQL queries.
- Git-like Data Branching - Create snapshots of datasets for safe experimentation, testing, and development without affecting production data.
- Auto Schema Evolution - Automatically adapt table schemas as data structures change, reducing ETL pipeline fragility.
- Snowflake-Compatible SQL - Full SQL compliance with familiar syntax, enabling seamless migration from Snowflake or other cloud warehouses.
- Arrow Flight Integration - High-performance data transport between Databend and external compute sandboxes via Arrow Flight protocol.
- Cloud-Native S3 Architecture - Storage and compute decoupled; scales elastically on S3, Azure Blob, or GCS with no infrastructure management.
Common Use Cases
- Building AI Agents with SQL Orchestration - A developer uses Databend’s Python UDF sandboxes to run LLM-powered agents that query enterprise data, call external APIs, and return structured responses via SQL functions.
- Running Real-Time RAG Pipelines - A data scientist queries vector embeddings stored in Databend to power retrieval-augmented generation systems without exporting data to separate vector databases.
- Enterprise BI with Zero ETL - A business analyst connects Tableau or Metabase directly to Databend to build dashboards on S3-based data lakes, leveraging Snowflake-compatible SQL and auto-scaling compute.
- Safe Experimentation on Production Data - A machine learning team creates a data branch to test new feature engineering logic or model inputs without risking live analytics or customer-facing reports.
Under The Hood
Architecture
- Modular monorepo with distinct, independently versioned crates for query engine, metadata service, and common utilities, enforcing clear boundaries and separation of concerns
- Transform-based query pipeline using Processor and Pipeline patterns to encapsulate execution stages like window functions and runtime filtering
- Dependency injection via Arc-wrapped contexts enabling pluggable optimizers and testable components
- Domain-driven meta-service with typed identifiers and abstracted KVAPI traits to ensure tenant isolation and storage agnosticism
- Storage layer decouples logical metadata from physical layout using versioned snapshots and constant-based prefix schemes
- Extensible plugin system via feature flags and enterprise modules that isolate advanced features without contaminating the core codebase
Tech Stack
- Rust-based core with a custom async runtime abstraction over Tokio, enforcing consistent concurrency patterns
- Modular workspace with over twenty crates organized by functional domain, including gRPC and metadata services
- Protocol Buffers and gRPC for internal service communication with auto-generated message types across components
- Comprehensive tooling stack including clippy, rustfmt, ruff, and custom lints to enforce idiomatic Rust and ban direct Tokio usage
- Python integration via bendpy for scripting and testing, supported by modern dependency management with uv and Handlebars for dynamic result rendering
- Dual licensing strategy enforced through tooling hooks, separating open-core functionality from enterprise extensions
Code Quality
- Extensive test coverage spanning unit, integration, and end-to-end scenarios with SQL logic and edge-case validation
- Strong type safety and protocol compatibility ensured through explicit serialization/deserialization implementations
- Robust error handling with custom error types and structured codes that validate both success and failure paths in query execution
- Consistent, domain-focused naming and file organization across Rust and Python components
- Automated linting and validation suites that verify SQL semantics, data type conversions, and optimization correctness, including negative test cases
- Clear architectural boundaries between query engine, metadata storage, and client interfaces enabling independent evolution and testing
What Makes It Unique
- Native support for vector embeddings, JSON, and structured data within a unified SQL engine, eliminating ETL dependencies for multi-modal analytics
- Fine-grained workload isolation through dynamic memory tracking and resource groups, enabling true multi-tenant query execution
- Protocol-agnostic, versioned metadata layer with protobuf serialization ensuring seamless compatibility across distributed nodes
- Object tagging and reference tracking system for policy-based governance and data lineage without external tooling
- Pipeline-based MPP execution engine with composable processors and explicit waker systems for low-latency, high-throughput processing
- S3-native storage architecture with direct file registration and external stage management, enabling serverless analytics at scale