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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112
｜
第
4
章
训练数据集和模型的一个完美基础。我们会在下一小节了解使用
Amazon S3
构建数
据湖的更多优势。
让我们假设我们的应用持续接收数据（如我们网站上的客户交互，产品评价消息等）
并将数据以制表符分割值（
tab-separated values
，
TSV
）文件格式写入
S3
。
作为一个数据科学家或机器学习工程师，我们想要迅速探索原始数据集。我们会介
绍
Amazon Athena
并展示如何将
Athena
作为一个交互式查询服务来使用标准
SQL
分析
S3
数据，而无需移动数据。在第一步，我们将使用
Athena
注册
S3
存储桶中
的
TSV
数据，并在数据集上运行一些单独查询。我们也会展示如何轻易将
TSV
数
据转换成
Apache Parquet
，一种更利于查询的列文件结构。
我们的商业智能团队或许也需要访问数据仓库中的一部分数据，他们可以使用标
准
SQL
客户端转换并查询数据，来创建报表并将趋势可视化。我们会介绍
Amazon
Redshift
，一个完全托管的数据仓库服务，以及如何将
TSV
数据插入到
Amazon
Redshift
并使用
Amazon Redshift Spectrum
将数据仓库和
S3
数据湖上较少被访问的
数据结合进行查询。商业智能团队也可以使用
Amazon Redshift
的数据湖导出功能
以将转换并丰富过的数据，以
Parquet
文件格式传输回
S3
数据湖。
我们会介绍一些技巧来使用压缩算法提升性能，以及利用
S3 Intelligent-Tiering
降低
开销。在第
12
章，我们会深入了解如何保证数据集安全、追踪数据访问以及在存储 ...
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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.