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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探索数据集
｜
191
值分布
唯一值
总计
最小值 中位数 平均值 模 最大值
图 5-23：Glue DataBrew 突出了 1 ～ 5 分星级评分之间的分类不平衡
5.9
节省开支并提升性能
在本节，我们提供一些在数据探索过程中降低开销并提升性能的技巧和窍门。我
们可以通过近似计数优化大数据集上昂贵的
SQL
COUNT
查询。通过使用
Redshift
AQUA
，我们可以减少网络
I/O
并提升查询性能。如果我们觉得
QuickSight
仪表能
够从性能提升中获益，我们应考虑启用
QuickSight SPICE
。
5.9.1
为非敏感的
Athena
查询结果共享
S3
存储桶
通过为整个团队的
Athena
查询结果选择共享
S3
位置，我们可以重用缓存的查询结
果，改善查询性能，并节省数据传输的开销。以下示例代码突出了
s3_staging_dir
，
其可以在不同团队成员之间共享以通过重用缓存结果提升经常执行查询的性能：
from pyathena import connect
import pandas as pd
# Set the Athena query results S3 bucket
s3_staging_dir = 's3://{0}/athena/staging'.format(bucket)
192
｜
第
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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.