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README
Apache-2.0

Datavines

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Datavines 是一站式开源数据可观测性平台,提供元数据管理、数据概览报告、数据质量管理,数据分布查询、数据趋势洞察等核心能力,致力于帮助用户全面地了解和掌管数据,让您做到心中有数。

架构设计

DataVinesArchitecture

安装

使用Maven3.6.1以及以上版本

$ mvn clean package -Prelease -DskipTests

特性

数据目录

  • 定时获取数据源元数据,构造数据目录
  • 定时监听元数据变更情况
  • 支持元数据的标签管理

数据目录

数据质量监控

  • 内置 27 个数据质量检查规则,开箱即用
  • 支持 4 种数据质量检查规则类型
    • 单表单列检查类型
    • 单表自定义SQL检查类型
    • 跨表准确性检查类型
    • 两表值比对检查类型
  • 支持配置定时任务进行定时检查
  • 支持配置 SLA 用于检查结果告警

数据质量检查

数据概览

  • 支持定时执行数据探测,输出数据概览报告
  • 支持自动识别列的类型自动匹配合适的数据概况指标
  • 支持表行数趋势监控
  • 支持列的数据分布情况查看

数据目录

插件化设计

平台以插件化设计为核心,以下模块都支持用户自定义插件进行扩展

  • 数据源:已支持 MySQLImpalaStarocksDorisPrestoTrinoClickHousePostgreSQL
  • 检查规则:内置空值检查、非空检查、枚举检查等27个检查规则
  • 作业执行引擎:已支持SparkLocal两种执行引擎。Spark 引擎目前仅支持Spark2.4版本,Local 引擎则是基于JDBC开发的本地执行引擎,无需依赖其他执行引擎。
  • 告警通道:已支持邮件
  • 错误数据存储:已支持 MySQL本地文件(仅支持Local执行引擎)
  • 注册中心:已支持 MySQLPostgreSQLZooKeeper

多种运行模式

  • 提供Web页面配置检查作业、运行作业、查看作业执行日志、查看错误数据和检查结果
  • 支持在线生成作业运行脚本,通过 datavines-submit.sh 来提交作业,可与调度系统配合使用

作业脚本

容易部署&高可用

  • 平台依赖少,容易部署
  • 最小仅依赖 MySQL 既可启动项目,完成数据质量作业的检查
  • 支持水平扩容,自动容错
  • 无中心化设计Server 节点支持水平扩展提高性能
  • 作业自动容错,保证作业不丢失和不重复执行

环境依赖

  1. Java 运行环境:Jdk8
  2. Datavines 支持 JDBC 引擎,如果你的数据量较小或者只是想做功能验证,可以使用 JDBC 引擎
  3. 如果您要想要基于 Spark 来运行 Datavines ,那么需要保证你的服务器具有运行 Spark 应用程序的条件

快速入门

请参考官方文档:快速入门指南

开发指南

请参考官方文档:开发指南

贡献指南

PRs Welcome

你可以提交 pull requests 或者 GitHub issues.

如果您是发布问题的新手,我们要求您阅读 How To Ask Questions The Smart Way (本指南不提供此项目的实际支持服务!)和How to Report Bugs Effectively 。好的错误报告可以让我们更好地帮助您!

感谢所有已经为 Datavines 做出贡献的人!

contrib graph

License

Datavines 基于 Apache License 2.0 协议。Datavines 依赖了一些第三方组件,它们的开源协议也为 Apache License 2.0 或者兼容 Apache License 2.0, 此外 Datavines 也直接引用或者修改了 Apache DolphinSchedulerSeaTunnel 以及 Dubbo 中的一些代码,均为 Apache License 2.0 协议的,感谢这些项目的贡献。

社交媒体

  • 微信公众号(中文,扫描二维码关注)

微信二维码

联系作者

  • 添加时备注:Datavines

wechat-author-qrcode

友情赞助

wechat-donation-qrcode

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简介

新一代数据可观测性系统,提供元数据管理和数据质量检查功能,让您心中有数! 展开 收起
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