Databend Products

Welcome to the Databend (pronounced as /ˈdeɪtəˌbɛnd/) documentation.

Databend is an open-source, cloud-native data warehouse built in Rust and fully based on object storage, evolved from a single analytics engine into a unified multimodal database. With a unified optimizer, compute, and storage engine, it supports BI Analytics, AI Vectors, Full-Text Search, and Geospatial Analysis on one platform through Snowflake-compatible SQL, and enables real-time data ingestion, transformation, and analysis via Stream + Task.

Product Features

Unified Engine

Analytics, vector, search, and geo share one optimizer and runtime.

Unified Data

Structured, semi-structured, unstructured, and vector data share object storage.

Analytics Native

ANSI SQL, windowing, incremental aggregates, and streaming power BI.

Vector Native

Embeddings, vector indexes, and semantic retrieval all run in SQL.

Search Native

Full-text search and inverted indexes fuel hybrid retrieval.

Geo Native

Geospatial indexes and functions power map and location services.

Getting Started

Create a Databend Cloud account or deploy your own Databend instance.

Start with Databend Cloud

Get started in minutes with our fully-managed cloud service. No setup required.

What you need to know:

• Choose Your Edition
• Pricing & Plans
• Using Databend Cloud

Deploy Your Own Instance

Install Databend on your infrastructure for complete control and customization.

What you need to know:

• 5-Minute Quick Start
• Download & Install
• Enterprise Features & Licensing

Connect to Databend

Connect your application to Databend in just a few minutes.

Developer Resources →

BendSQL

Java

Golang

Python

Node.js

Rust

MCP Server

Load Data into Databend

Bulk import data into Databend(Cloud) in multiple formats.

Know More →

Kafka

dbt

Airbyte

Flink CDC

Addax

DataX

Debezium

Tapdata

Vector

AI & BI & Visualization & Notebooks

Databend offers connectors and plugins for integrating with major data import tools, ensuring efficient data synchronization.

All Tools →

Deepnote

Jupyter

Metabase

Grafana

Redash

Superset

Tableau

MindsDB

Continuous Data Pipelines

Data pipelines automate the process of moving and changing data from different sources into Databend.

Know More →

Real-Time CDC Ingestion

Automated Data Pipelines

Additional Information

AI Capabilities

Security

Contact Support

Use Cases

FAQ

Changelog

Here are some entries you might want to learn about

Getting Started

• Quick Start: Launch Databend with Docker and load sample data fast.
• Databend Cloud: Spin up serverless warehouses and manage your organization.
• Connect to Databend: Connect with various SQL clients and programming languages.
• SQL Reference: Browse Databend SQL commands, functions, and syntax.

Data Processing

• Data Loading: Import data from various sources into Databend.
• Data Unloading: Export data from Databend to different formats.
• Semi-Structured Data: Process JSON, arrays, and nested data with VARIANT type.

Unified Workloads

• SQL Analytics Guide: Shared session tables for analytics, search, vector, and geo workloads.
• JSON & Search Guide: Query VARIANT payloads with inverted indexes and Elasticsearch-style QUERY.
• Vector Database Guide: Store embeddings and run semantic similarity inside Databend.
• Geo Analytics Guide: Map incidents with geospatial SQL for real-time insights.
• Lakehouse ETL Guide: Stream object storage files into managed tables without silos.

Performance & Scale

• Performance Optimization: Enhance query performance with various strategies.
• Benchmarks: Compare Databend performance with other data warehouses.
• Data Lakehouse: Seamless integration with Hive, Iceberg, and Delta Lake.

Community & Support

• Join Slack: Chat with the Databend community and core engineers.
• Docs Issues: Report problems or request new coverage.
• Roadmap: Track upcoming features and share feedback.
• Email Us: Reach the team directly when you need help.