Databend 产品

欢迎来到 Databend (发音为 /ˈdeɪtəˌbɛnd/) 文档。

Databend 是一个开源的、Serverless 产品形态、存算分离架构、基于对象存储构建的云原生数据湖仓，提供极致的性能、快速的弹性扩展能力，致力于打造开源版的 Snowflake。

Introduction to Databend Products

Check out the products you can choose from.

Getting Started

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

Activate Databend Cloud

This topic outlines the steps for applying for beta access as an organization.

Topics you might find useful:

Self-Hosted Databend

This topic provide detailed instructions for deploying and upgrading Databend.

Topics you might find useful:

Connect to Databend

Connect your application to Databend in just a few minutes.

Developer Resources →

Load Data into Databend

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

AI & BI & Visualization & Notebooks

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

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

Product Features

Serverless Elastic Architecture

Stateless compute nodes with serverless elasticity enable rapid scaling and recovery based on workload.

Object Storage

Stores all data in object storage.

Parquet Data Format

Uses the standard Parquet format, eliminating data lock-in risks.

Decoupled Storage and Compute

Designed for multi-tenant data sharing with a truly decoupled architecture, enabling data marketplace functionality.

Open Source with Flexible Deployment

Supports both public cloud SaaS and private deployments, with an open-source core to avoid vendor lock-in.

Data Lake Support

Reads data from Iceberg, Hive, and Delta Lake seamlessly.

以下是一些您可能想要了解的入口

SQL 参考: 您快速访问 Databend 基础知识的指南！
连接到 Databend: 使用各种 SQL 客户端和编程语言进行连接。
数据加载: 从各种数据源导入数据到 Databend。
数据卸载: 将数据从 Databend 导出为不同格式。
外部函数: 使用自定义函数扩展 Databend 的功能。
Databend AI 和 ML: 在数据处理中利用 AI 功能。
数据管理: 有效管理您的数据生命周期。
数据湖仓: 与 Hive、Iceberg 和 Delta Lake 的无缝集成。
性能优化: 通过各种策略提升查询性能。
基准测试: 将 Databend 性能与其他数据仓库进行比较。