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下表列出了可用的开放源码数据质量软件发行版，涵盖了数据质量评估的某些方面。
纳入标准
- 在其中一个存储库中可公开访问的任何开放源代码发行版。为简洁起见，当存储库包含许多不同的工具时，只提供一个链接
- 库/框架不必只关注数据质量，因为功能经常与数据清理或探索性数据分析捆绑在一起。
- 数据质量评估在广泛不同的环境/工作流程（从验证excel表到大数据管道，离线/在线等）中非常重要，因此该列表包含了不同的集合
- star/issue/fork计数作为成熟度的粗略衡量标准。使用风险自负
开源数据质量软件
| 1. Name | 2. Description | 3. Language | 4. Online Docs | 5. URL | 6. Stars | 7. Issues | 8. Forks |
|---|---|---|---|---|---|---|---|
|
awslabs/ deequ |
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets | Scala | github | 1328 | 90 | 256 | |
|
data-cleaning/ validate |
validate: Data cleaning for statistical purposes | R | docs | github | 236 | 21 | 18 |
|
datacleaner/ DataCleaner |
DataCleaner Community Edition | Java | docs | github | 371 | 172 | 136 |
|
daveoncode/ pyvaru |
pyvaru: Rule based data validation library for python | Python | docs | github | 14 | 1 | 3 |
|
great-expectations/ great_expectations |
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling | Python | docs | github | 3127 | 147 | 348 |
|
OpenRefine/ OpenRefine |
openRefine is a tool for working with messy data | Java | docs | github | 7735 | 595 | 1376 |
|
pandas-profiling/ pandas-profiling |
pandas-profiling generates profile reports from a pandas DataFrame | Python | docs | github | 6338 | 44 | 962 |
| pyeve/cerberus | cerberus is a lightweight, extensible data validation library for Python | Python | docs | github | 2246 | 33 | 202 |
|
ResidentMario/ missingno |
missingno is a missing data visualization module for Python | Python | github | 2540 | 15 | 334 | |
|
WeBankFinTech/ Qualitis |
Qualitis is a data quality management platform that supports quality verification, notification, and management for various datasources | Java | docs | github | 208 | 16 | 107 |
|
whylabs/ whylogs-python |
whylogs-python is a Python implementation of whylogs | Python | docs | github | 191 | 10 | 7 |
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原文地址
https://www.openriskmanual.org/wiki/Open_Source_Data_Quality_Software
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