January 2018
Beginner to intermediate
234 pages
7h 8m
Chinese
Content preview from Kafka权威指南
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104
｜
第
7
章
7.1.5
转换
数据转换比其他需求更具争议性。数据管道的构建可以分为两大阵营，即
ETL
和
ELT
。
ETL
表示
提取
—
转换
—
加载
（
Extract-Transform-Load
），也就是说，当数据流经数据管道
时，数据管道会负责处理它们。这种方式为我们节省了时间和存储空间，因为不需要经过
保存数据、修改数据、再保存数据这样的过程。不过，这种好处也要视情况而定。有时
候，这种方式会给我们带来实实在在的好处，但也有可能给数据管道造成不适当的计算和
存储负担。这种方式有一个明显不足，就是数据的转换会给数据管道下游的应用造成一些
限制，特别是当下游的应用希望对数据进行进一步处理的时候。假设有人在
MongoDB
和
MySQL
之间建立了数据管道，并且过滤掉了一些事件记录，或者移除了一些字段，那么
下游应用从
MySQL
中访问到的数据是不完整的。如果它们想要访问被移除的字段，只能
重新构建管道，并重新处理历史数据（如果可能的话）。
ELT
表示
提取
—
加载
—
转换
（
Extract-Load-Transform
）。在这种模式下，数据管道只做少量的
转换（主要是数据类型转换），确保到达数据池的数据尽可能地与数据源保持一致。这种
情况也被称为高保真（
high fidelity
）数据管道或数据湖（
data lake
）架构。目标系统收集
“原始数据”，并负责处理它们。这种方式为目标系统的用户提供了最大的灵活性，因为它
们可以访问到完整的数据。在这些系统里诊断问题也变得更加容易，因为数据被集中在同
一个系统里进行处理，而不是分散在数据管道和其他应用里。这种方式的不足在于 ...
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