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databendlabs/databend

★ 9,285RustAudience · dataComplexity · 4/5LicenseSetup · moderate

TLDR

An open-source cloud data warehouse built in Rust that queries large datasets on S3, Azure, or GCS with SQL, adds AI function support via Python sandbox scripts, vector search, and Git-like data branching.

Mindmap

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  root((repo))
    What it does
      Cloud data warehouse
      Vector full-text search
      Data branch snapshots
    Tech stack
      Rust engine
      SQL interface
      Python UDF sandbox
    Storage backends
      Amazon S3
      Azure Blob
      Google Cloud Storage
    Getting started
      pip install local
      Docker local run
      Hosted cloud service
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Things people build with this

USE CASE 1

Run SQL analytics on terabytes of data in Amazon S3 without managing separate database infrastructure.

USE CASE 2

Call an AI model from inside a SQL query using a Python sandbox function applied to an entire table.

USE CASE 3

Create a snapshot branch of production data to test a transformation safely without touching live data.

USE CASE 4

Run full-text search and vector similarity search on the same dataset using standard SQL statements.

Tech stack

RustPythonSQLDocker

Getting it running

Difficulty · moderate Time to first run · 30min

Production use requires cloud object storage (S3, Azure Blob, or GCS), a local pip install is available for development and testing.

Dual-licensed under Apache 2.0 and Elastic License 2.0, the Elastic license restricts using Databend as the basis for a competing hosted service.

In plain English

Databend is an open-source data warehouse built in Rust, designed to store and analyze large amounts of data stored in cloud object storage like Amazon S3, Azure Blob, or Google Cloud Storage. A data warehouse is a database system designed for analytical queries, meaning it is optimized for reading and summarizing large datasets rather than for fast individual record lookups. Databend handles that kind of workload while also adding vector search and full-text search in the same engine, so you do not need separate systems for those tasks. One of the distinctive features is what the README calls "agent-ready" architecture. You can write Python functions inside the database using a feature called sandbox UDFs (user-defined functions). Those functions run in isolated containers, and you call them from regular SQL queries. The example in the README shows defining a function that could call an AI model and then running it over a table of data with a single SQL statement. This lets you combine data processing and AI logic without moving data to a separate application. Data branching is also supported, described as working like version control for data. You can create a snapshot of production data and let processes run on that snapshot without affecting the live data, similar to creating a branch in code version control. Getting started is quick: there is a Python package you can install with pip for local development, a Docker image for running the full system locally, and a hosted cloud service. The cloud version is described as production-ready in about sixty seconds. The project is dual-licensed under Apache 2.0 and Elastic 2.0. An enterprise edition with additional support options is available from the company behind the project.

Copy-paste prompts

Prompt 1
How do I install Databend locally with pip and run my first SQL query on a Parquet file stored in S3?
Prompt 2
Show me how to write a Python sandbox UDF in Databend that calls an OpenAI API and apply it to every row in a customer table.
Prompt 3
How do I create a data branch in Databend to test a transformation on a snapshot without modifying my production table?
Prompt 4
Set up Databend with Docker and connect to it from a Python script to insert and query rows.
Prompt 5
What is the difference between the Apache 2.0 and Elastic 2.0 licenses that Databend uses, and which applies to my use case?
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