/databend

A modern cloud data warehouse focusing on reducing cost and complexity for your massive-scale analytics needs. Open source alternative to Snowflake. Also available in the cloud: https://app.databend.com

Primary LanguageRustOtherNOASSERTION

The Future of Cloud Data Analytics

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

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databend

What is Databend?

Databend is an open-source Elastic and Workload-Aware modern cloud data warehouse focusing on Low-Cost and Low-Complexity for your massive-scale analytics needs.

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).

  • 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.

  • MySQL/ClickHouse Compatible

    Databend is ANSI SQL compliant and MySQL/ClickHouse wire protocol compatible, making it easy to connect with existing tools(MySQL Client, ClickHouse HTTP Handler, Vector, DBeaver, Jupyter, JDBC, etc.).

  • 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

databend-arch

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

Deploying Databend
Connecting to Databend
Loading Data into Databend
Managing Users
Managing Databases
Managing Tables
Managing Data
Managing Views
AI Functions
Performance

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:

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:

Roadmap

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