February 2024
Intermediate to advanced
370 pages
7h
Chinese
Content preview from 数据工程之道：设计和构建健壮的数据系统
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320
|
第
9
章
•
MLOps/
机器学习工程师
•
业务侧
—
非数据或非技术的利益相关者、经理和高管
需要注意，数据工程师更多的是在
支持
这些利益相关者的工作，不一定对数据的最终使
用方式负责。例如，数据工程师为分析师解读的报告提供数据服务，但数据工程师并不
对这些解读负责。数据工程师负责的是产出高质量的数据产品。
数据工程走到了交付阶段后会产生反馈循环。数据很少以静态存在，外部环境会影响到
被获取和提供的数据，以及被二次获取和提供的数据。
在数据服务阶段，数据工程师的一项重要任务是将职责和工作内容分离。在初创公司，
数据工程师可能需要兼任机器学习工程师或数据科学家，但这不是长久之计。公司发展
壮大后，会更需要与其他数据团队成员建立明确的职责分工。
采用数据网格会在很大程度上重新分配团队职责，每个领域的团队都需要承担各种提供
数据服务的任务。为了使数据网格顺利运转，每个团队都必须切实履行数据服务职责，
并且通力合作以确保公司取得成功。
9.8
底层设计
数据服务是数据工程生命周期底层设计的最后一部分内容。数据生命周期是一个闭环，
在环中的一切都是一脉相承的。很多情况都是到了数据服务阶段才发现前期的漏洞。因
此，数据工程师需要一直寻找底层设计框架下能够帮助数据产品提升的方法。
“数据是一个无声的杀手”，之前章节提到的底层设计运用直到数据服务阶段才会一览无
余。提供数据服务是确保交付到终端用户手中的数据质量的最后一道屏障。
9.8.1
安全
无论是与人还是与系统共享数据，都适用同样的安全原则。有很多不分青红皂白地共享 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.