Snowflake ：2025数据工程权威指南：AI时代韧性数据系统构建：从基础到最佳实践（英文版）（22页）.pdf

1、ESSENTIAL GUIDE DATA ENGINEERINGFrom basics to best practices,how to build resilient data systems in the AI eraTABLE OF CONTENTSThe Changing Role of Data Engineers .3The Rise of ModernData Engineering .5Building New Pipelines .8Managing Legacy Pipelines .14Architecture and Interoperability .15DevOps

2、 in Data Engineering .18Make Your Data AI Ready .21Table of Contents|2 THE ESSENTIAL GUIDE TO DATA ENGINEERINGTHE CHANGING ROLE OF DATA ENGINEERSArchitects.Foundation-setters.Builders.Orchestrators.Todays data engineers are all of these things and more.Simply put:They are the operational lifeblood o

3、f any data-driven organization.To this point,consider an interesting wrinkle that emerged from a recent Snowflake sponsored MIT Technology Review Insights report,“Redefining Data Engineering in the Age of AI.”In a survey of 400 executives and data engineers,74%of respondents agreed that AI tools hav

4、e helped data engineering teams do more work:deliver more projects,generate new code and altogether be more productive.And yet,just as many(77%)also agreed that data engineers were seeing“increasingly heavy”workloads(34%of them agreed strongly,in fact).If data engineers are doing more work and also

5、seeing their workloads increase,then thats a telltale sign of exponential growth.Its a clear indication that in an AI-driven world,the role of the data engineer is expanding.With more demands and more responsibility,they are becoming essential to overall business success.Indeed,a strong data enginee

6、ring function enables organizations to speed up time to value and clear bottlenecks that slow productivity.They hold the keys to faster insights,building workflows that operate efficiently rather than Rube Goldberg machines of tenuous dependencies.With an increasing demand for real-time,data-driven