Databend
ANY DATA. ANY SCALE. ONE DATABASE.
Blazing analytics, fast search, geo insights, vector AI — supercharged in a new-era Snowflake-compatible warehouse
Why Databend?
Databend has expanded from analytics into a unified multimodal database: one Snowflake-compatible SQL surface for BI, AI, search, and geospatial workloads.
Unified Engine: Analytics, vector, full-text, and geospatial share the same optimizer and elastic runtime.
Unified Data: Structured, semi-structured, vector, and unstructured live directly on object stores with indexes, caching, transactions, MVCC branching.
Analytics Native: ANSI SQL, windowing, incremental aggregates, and streaming ingestion deliver BI without moving data.
Vector Native: Built-in embeddings, vector indexes, and semantic retrieval exposed through SQL and SDKs.
Search Native: JSON full-text indexing, structured filters, and ranking to power hybrid search experiences.
Unified Deployment: Cloud, self-hosted, or pip install databend all run the same engine on shared object storage.
Rust Performance: Vectorized Rust execution with separated storage keeps performance high and compute spend lean.
Enterprise Scale: Fine-grained governance, masking, auditing, and production deployments exceeding 800+ PB and 100M+ daily queries.
Benchmarks
Performance: TPC-H vs Snowflake | ClickBench Results Cost: 90% Cost Reduction
Use Cases
- SQL Analytics: ANSI joins, window functions, incremental aggregates, and streaming ingestion for BI workloads.
- AI Vector: Persist embeddings alongside facts, index vectors, and run semantic retrieval for RAG and agent pipelines.
- JSON Search: Mix full-text search over JSON, metadata filters, and semantic similarity inside one query plan.
- Geo Analytics: Run distance, containment, and hex-grid analytics to power maps and mobility scenarios.
- Lakehouse ETL: Query Parquet, CSV, and NDJSON in object storage, transform streaming updates, and load them into managed tables.
Quick Start
Option 1: Databend Cloud Warehouse (Recommended)
Start with Databend Cloud - Serverless warehouse clusters, production-ready in 60 seconds
Option 2: Local Development with Python
pip install databend
import databend
ctx = databend.SessionContext()
# Local table for quick testing
ctx.sql("CREATE TABLE products (id INT, name STRING, price FLOAT)").collect()
ctx.sql("INSERT INTO products VALUES (1, 'Laptop', 1299.99), (2, 'Phone', 899.50)").collect()
ctx.sql("SELECT * FROM products").show()
# S3 remote table (same as cloud warehouse)
ctx.create_s3_connection("s3", "your_key", "your_secret")
ctx.sql("CREATE TABLE sales (id INT, revenue FLOAT) 's3://bucket/sales/' CONNECTION=(connection_name='s3')").collect()
ctx.sql("SELECT COUNT(*) FROM sales").show()
Option 3: Docker (Self-Host Experience)
docker run -p 8000:8000 datafuselabs/databend
Experience the full warehouse capabilities locally - same features as cloud clusters.
Community
- 📖 Documentation - Complete guides and references
- 💬 Slack - Live community discussion
- 🐛 GitHub Issues - Bug reports and feature requests
- 🎯 Good First Issues - Start contributing today
Contributors get immortalized in system.contributors table! 🏆
📄 License
Apache License 2.0 + Elastic License 2.0 Licensing FAQs