databend

databend

𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲. Open-source Snowflake alternative. Proven at petabyte scale with enterprise performance. Built for multimodal analytics. https://databend.com

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Databend is an open-source cloud data warehouse built in Rust, offering fast query execution and data ingestion for complex analysis of large datasets. It integrates with major cloud platforms, provides high performance with AI-powered analytics, supports multiple data formats, ensures data integrity with ACID transactions, offers flexible indexing options, and features community-driven development. Users can try Databend through a serverless cloud or Docker installation, and perform tasks such as data import/export, querying semi-structured data, managing users/databases/tables, and utilizing AI functions.

README:

Databend

ANY DATA. ANY SCALE. ONE DATABASE.

Multimodal data warehouse for the AI era with Snowflake-compatible SQL

☁️ Try Cloud🚀 Quick Start📖 Documentation



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databend

Why Databend?

Multimodal Data Warehouse: Analyze structured, semi-structured, vector, and geospatial data with unified Snowflake-compatible SQL.

AI-Native Platform: Built-in vector search, AI functions, embedding generation, and full-text search - no separate systems needed.

10x Faster & 90% Cost Reduction: Rust-powered vectorized execution with S3-native storage eliminates vendor lock-in and proprietary overhead.

Deploy Anywhere, Connect Everything: 100% open source - run locally with pip install databend, self-host, or use managed cloud clusters. All instances share the same data seamlessly.

Production Proven: Trusted by world-class enterprises managing 800+ petabytes and 100+ million queries daily.

Enterprise Ready: Fine-grained access control, data masking, and audit logging with complete data sovereignty.

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.

Benchmarks

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

Architecture

Databend Architecture

Multimodal Cloud Warehouse: Production clusters analyze structured, semi-structured, vector, and geospatial data with Snowflake-compatible SQL. Local development environments can attach to the same warehouse data for seamless development.

Use Cases

  • Data Analytics: Snowflake alternative with significant cost reduction
  • AI/ML Pipelines: Vector search and AI functions built-in
  • Real-time Analytics: High-performance queries on petabyte-scale data
  • Data Lake Analytics: Query Parquet, CSV, TSV, NDJSON, Avro, ORC directly from S3

Community

Contributors get immortalized in system.contributors table! 🏆

📄 License

Apache License 2.0 + Elastic License 2.0 Licensing FAQs


Built by engineers who redefine what's possible with data
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