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.

Performance: TPC-H vs Snowflake | ClickBench Results Cost: 90% Cost Reduction

Databend Architecture

• 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.

Start with Databend Cloud - Serverless warehouse clusters, production-ready in 60 seconds

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()

docker run -p 8000:8000 datafuselabs/databend

Experience the full warehouse capabilities locally - same features as cloud clusters.

• 📖 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! 🏆

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