The Future of Cloud [Data+AI] Analytics

Databend Serverless Cloud (beta) | Documentation | Benchmarking | Roadmap (v1.3)

What is Databend?

Databend is an open-source Elastic and Workload-Aware modern cloud data warehouse that focuses on Low-Cost and Low-Complexity for your massive-scale analytics needs. The open-source alternative to Snowflake, Databend is crafted in Rust, enabling superior performance and efficiency.

Databend uses the latest techniques in vectorized query processing to allow you to do blazing-fast data analytics on object storage: (S3, Azure Blob, Google Cloud Storage, Alibaba Cloud OSS, Tencent Cloud COS, Huawei Cloud OBS, Cloudflare R2, Wasabi or MinIO).

• Lakehouse Architecture

  Databend's Lakehouse architecture combines the scalability of data lakes with the speed of data warehouses, supporting data reads from Hive and Iceberg for streamlined accessibility and flexibility.

• Feature-Rich

  Support for atomic operations including SELECT/INSERT/DELETE/UPDATE/REPLACE/COPY/ALTER and advanced features like Time Travel, Multi Catalog(Apache Hive/Apache Iceberg).

• Instant Elasticity

  Databend completely separates storage from compute, which allows you easily scale up or scale down based on your application's needs.

• Blazing Performance

  Databend leverages data-level parallelism(Vectorized Query Execution) and instruction-level parallelism(SIMD) technology, offering blazing performance data analytics.

• Git-like MVCC Storage

  Databend stores data with snapshots, enabling users to effortlessly query, clone, or restore data from any history timepoint.

• Support for Semi-Structured Data

  Databend supports ingestion of semi-structured data in various formats like CSV, JSON, and Parquet, which are located in the cloud or your local file system; Databend also supports semi-structured data types: ARRAY, TUPLE, MAP, JSON, which is easy to import and operate on semi-structured.

• Easy to Use

  Databend has no indexes to build, no manual tuning required, no manual figuring out partitions or shard data, it’s all done for you as data is loaded into the table.

Architecture

Try Databend

1. Databend Serverless Cloud

The fastest way to try Databend, Databend Cloud

2. Install Databend from Docker

Prepare the image (once) from Docker Hub (this will download about 170 MB data):

docker pull datafuselabs/databend

To run Databend quickly:

docker run --net=host  datafuselabs/databend

Getting Started

Contributing

Databend is an open source project, you can help with ideas, code, or documentation, we appreciate any efforts that help us to make the project better! Once the code is merged, your name will be stored in the system.contributors table forever.

To get started with contributing:

• Building Databend From Source
• The First Good Pull Request

Community

For general help in using Databend, please refer to the official documentation. For additional help, you can use one of these channels to ask a question:

• 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!)

Roadmap

• Roadmap v1.3
• Roadmap v1.2
• Roadmap v1.1
• Roadmap v1.0
• Roadmap v0.9
• Roadmap 2023

License

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.

Acknowledgement

• Databend is inspired by ClickHouse and Snowflake.
• Databend's computing model is based on Arrow2, Arrow2 is a faster and safer implementation of the Apache Arrow Columnar Format.
• The documentation website is hosted by Vercel.