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数据工程之道:设计和构建健壮的数据系统
book

数据工程之道:设计和构建健壮的数据系统

by Joe Reis, Matt Housley
February 2024
Intermediate to advanced
370 pages
7h
Chinese
China Machine Press
Content preview from 数据工程之道:设计和构建健壮的数据系统
数据工程生命周期
|
59
Personally Identifiable Information
PII
)和其他敏感信息,可以在转换数据集时识别和
跟踪偏见。监管要求和合规处罚只会越来越多。确保你的数据资产符合越来越多的数据
法规,如
GDPR
CCPA
。请认真对待这件事。我们在整本书中提供了技巧,以确保你
将道德和隐私纳入数据工程生命周期。
2.2.3 DataOps
DataOps
将敏捷方法、
DevOps
和统计过程控制(
Statistical Process Control
SPC
)的最
佳实践映射到数据。
DevOps
旨在提高软件产品的发布和质量,而
DataOps
则针对数据
产品也是做同样的事情。
数据产品与软件产品的区别在于数据的使用方式。软件产品为终端用户提供特定的功能
和技术特性。相比之下,数据产品是围绕合理的业务逻辑和指标建立的,其用户可以做
出决策或构建执行自动化操作的模型。数据工程师必须了解构建软件产品的技术方面以
及将创建优秀数据产品的业务逻辑、质量和指标。
DevOps
一样,
DataOps
大量借鉴了精益生产和供应链管理,混合人员、流程和技术
以缩短实现价值的时间。正如
Data Kitchen
DataOps
专家)所描述的那样
7
1
DataOps 是技术实践、工作流、文化规范和架构模式的集合,能够实现:
快速创新和实验,以更快的速度为客户提供新的见解
极高的数据质量和极低的错误率
跨复杂的人员、技术和环境阵列进行协作
结果的清晰测量、监控和透明度
精益实践(如缩短交货时间和最大限度地减少缺陷 ...
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Publisher Resources

ISBN: 9787111745273