September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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将数据接入云
｜
135
用户和组来管理数据库访问。如果我们需要在多个事业部共享数据，或需要从中心
数据仓库集群共享数据到额外的
BI
和分析集群，数据共享功能将十分有用。
4.5
在
Amazon Athena
和
Amazon Redshift
之间
选择
当需要在
Amazon S3
保存的数据上运行按需
SQL
查询时，
Amazon Athena
是首选。
其不需要我们设置或管理任何基础设施资源，也不需要移动任何数据。其支持结构
化、非结构化和半结构化数据。通过
Athena
，我们定义“读时模式”——我们只需
要登录，创建一个表，并开始运行查询。
Amazon Redshift
针对
PB
级结构化数据的现代数据分析。我们需要提前定义“写时
模式”。与无服务器的
Athena
不同，虽然
Amazon Redshift
需要我们在开始查询之
前创建集群（计算和存储资源），接入数据并构建表，但其能够照顾到性能和扩展性。
因此，在拥有事务性质（数据会被更新）的高度相关数据、需要复杂表连接的负载
或亚秒级时延要求的场景下，
Amazon Redshift
是最佳选择。
Athena
和
Amazon Redshift
均为读取繁重的分析负载而优化，其并不能替代写入繁
重的关系型数据库，例如
Amazon Relational Database
服务（
RDS
）和
Aurora
。概
括来说，使用
Athena
进行解释性分析和运行性调试，使用
Amazon Redshift
处理关
键业务的报表和大屏。
4.6
降低开销并提升性能
在本节，我们提供一些技巧和窍门，以在数据接入时降低开销并提升性能 ...
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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.