Databend, built in Rust, is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.
-
Cloud-Native: Integrates with AWS S3, Azure Blob, Google Cloud, and more.
-
High Performance: Rust-built, with cutting-edge, high-speed vectorized execution. 👉 ClickBench.
-
Cost-Effective: Designed for scalable storage and computation, reducing costs while enhancing performance. 👉 TPC-H.
-
AI-Powered Analytics: Enables advanced analytics with AI Functions.
-
Data Simplification: Streamlines data ingestion, no external ETL needed. 👉 Data Loading.
-
Real-Time CDC: Supports real-time incremental data updates to keep data current and accurate. 👉 Stream
-
Format Flexibility: Supports multiple data formats and types, including JSON, CSV, Parquet, GEO, and more.
-
ACID Transactions: Ensures data integrity with atomic, consistent, isolated, and durable operations.
-
Version Control: Provides Git-like version control for data, allowing querying, cloning, and reverting at any point.
-
Schemaless: VARIANT data type enabling schemaless data storage and flexible data modeling.
-
Flexible Indexing: Virtual Column, Aggregating Index, and Full-Text Index, for faster data retrieval.
-
Community-Driven: Join a welcoming community for a user-friendly cloud analytics experience.
The fastest way to try Databend, Databend Cloud
Prepare the image (once) from Docker Hub (this will download about 170 MB data):
docker pull datafuselabs/databendTo run Databend quickly:
docker run --net=host datafuselabs/databendConnecting to Databend
Data Import and Export
- How to load Parquet file into a table
- How to export a table to Parquet file
- How to load CSV file into a table
- How to export a table to CSV file
- How to load TSV file into a table
- How to export a table to TSV file
- How to load NDJSON file into a table
- How to export a table to NDJSON file
- How to load ORC file into a table
Loading Data From Other Databases
Querying Semi-structured Data
Visualize Tools with Databend
Managing Users
Managing Databases
Managing Tables
Managing Views
AI Functions
Data Management
Accessing Data Lake
Performance
Databend thrives on community contributions! Whether it's through ideas, code, or documentation, every effort helps in enhancing our project. As a token of our appreciation, once your code is merged, your name will be eternally preserved in the system.contributors table.
Here are some resources to help you get started:
For guidance on using Databend, we recommend starting with the official documentation. If you need further assistance, explore the following community channels:
- Slack (For live discussion with the Community)
- GitHub (Feature/Bug reports, Contributions)
- Twitter (Get the news fast)
- I'm feeling lucky (Pick up a good first issue now!)
Stay updated with Databend's development journey. Here are our roadmap milestones:
Databend is released under a combination of two licenses: the Apache License 2.0 and the Elastic License 2.0.
When contributing to Databend, you can find the relevant license header in each file.
For more information, see the LICENSE file and Licensing FAQs.
-
Inspiration: Databend's design draws inspiration from industry leaders ClickHouse and Snowflake.
-
Computing Model: Our computing foundation is built upon apache arrow.
-
Documentation Hosting: The Databend documentation website proudly runs on Vercel.