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
｜
127
4.4
用
Amazon Redshift Spectrum
构建数据湖仓
数据湖和数据仓库的一个根本区别在于，我们接入并存储海量的未处理原始数据到
数据湖，但通常我们只加载最近数据的一小部分到数据仓库。取决于我们的商业和
分析场景，这些数据可以来自过去几个月，一年或过去两年。让我们假设我们要将
Amazon
客户评价数据集中最近两年数据加载到数据仓库中，以分析逐年的客户行
为和评价趋势。我们将使用
Amazon Redshift
作为此例中的数据仓库。
Amazon Redshift
是一个完全托管的数据仓库，其允许我们在
PB
级的结构化、半结
构化和
JSON
数据上运行复杂的分析查询。
我们的查询被分布在多个节点上并行处
理。关系型数据库为了以行存储数据而优化，并在绝大多数情况下服务事务型应用，
与此相反，
Amazon Redshift
实现了列数据存储，这种方式为最关注每一列中的数据
的分析性应用而优化。
Amazon Redshift
也包含了
Amazon Redshift Spectrum
，其允许我们直接通过
Amazon Redshift
执行
SQL
查询保存于
Amazon S3
数据湖的
EB
级非结构化数据，
而无需转移数据的位置。
Amazon Redshift Spectrum
是湖仓架构的一部分，这种架
构将
S3
数据湖和
Amazon Redshift
数据仓库进行统一，包括共享的安全和基于行
-
列的访问控制。
Amazon Redshift Spectrum
支持多种开源存储框架，包括
Apache ...
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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.