February 2024
Intermediate to advanced
370 pages
7h
Chinese
Content preview from 数据工程之道：设计和构建健壮的数据系统
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119
台通常希望其服务接近客户现有云工作负载。出于这个原因，
Snowflake
和
Databricks
在多云中提供他们的
SaaS
产品。这对数据密集型应用程序来说尤其关键，因为网络延
迟和带宽限制阻碍了性能，而数据出口的成本又很高。
采用多云方法的另一个常见动机是利用几个云中的最佳服务。例如，一家公司可能希望
在
Google Cloud
上处理其谷歌广告和分析数据，并通过
GKE
部署
Kubernetes
。而该公
司也可能专门为微软的工作负载采用
Azure
。另外，该公司可能喜欢
AWS
，因为它有几
个一流的服务（如
AWS Lambda
），并有很大的心智份额，使其相对容易雇用精通
AWS
的工程师。各种云提供商服务的任何组合都是可能的。鉴于主要云提供商之间的激烈竞
争，预计它们会提供更多的最佳服务，使多云服务更加引人注目。
多云方法也有几个缺点。正如我们刚才提到的，数据出口成本和网络瓶颈是关键。走多
云路线会带来巨大的复杂性。公司现在必须在多个云上管理一系列令人眼花缭乱的服
务。跨云整合和安全是一个相当大的挑战。多云网络可能是非常复杂的。
新诞生的概念“云中云”服务是通过提供跨云的服务、在云之间无缝复制数据和一
个界面管理几个云上的工作负载来促进多云的使用，降低操作的复杂性。举个例子，
Snowflake
账户在单云区运行，但客户可以随时在
GCP
、
AWS
或
Azure
中启动其他账户。
Snowflake
在这些不同的云账户之间提供简单的预定的数据复制。在所有这些账户中，
Snowflake
的接口基本相同，消除了在云原生数据服务之间切换的培训负担。 ...
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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.