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数据工程的未来.pdf

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1、1Future Future of of DataDataEngineeringEngineeringDATA AND ANAYTI CS PERSPECTIVEDATA SUMMIT 2025MAY152Who We AreWere a global digital consultancy global digital consultancy transforming how the worlds leading enterprises and biggest brands connect with customers and grow their businesses.With Perfi

2、cient,you get experience and expertise,speed and agility,and a healthy dose of pragmatism to drive your business forward.3Who Am IJerry Locke(Snowflake Practice Director)Jerry Locke(Snowflake Practice Director)Been in data my entire 20+careerWorked in the cloud for the past 10(mostly in Snowflake)Ad

3、junct Professor at USD(University Of San Diego)Been part of hundreds of cloud deploymentsBelieve almost all human problems can be solved with data“If you dont measure,it can never get better”4MISSIONTo shatter boundaries,obsess over outcomes,and forge the future.To be the place where great mindsand

4、great companies converge to boldly advance business.VISIONPurposePurposeOurOur47Our PartnershipsSTRENGTHEN ED BY8A Glimpse Into Our A Glimpse Into Our Technology PartnerTechnology Partner EcosystemEcosystem9Data EngineeringMovement of data for decision making,analytics and reporting is framework the

5、 data industry has accepted.The near future still holds true with the outcomes.However,the volume,speed to decisions and AI frameworks will enable business insights in ways we have just begun to comprehend and utilize.10The future is now11Growth:Modern data engineering paradigm Growth:Modern data en

6、gineering paradigm Challenges Driving Our Industry:Global data volume is expected to reach 180 zettabytes this year(2025)Up from 64 zettabytes in 2020 IoT devices(rate of adoption globally is 2x since 2020)Video growthAI Generative and Agentic Data integration and source fragmentationData Quality La

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本文主要讨论了数据工程的未来趋势和挑战。文章指出,随着全球数据量的激增,物联网设备的广泛采用,以及视频和AI技术的快速发展,数据工程正面临诸多挑战。同时,数据集成和源碎片化、数据质量、治理的缺失以及工具的丰富和滥用也是行业需要面对的问题。 文章预测,在未来2至5年内,服务器less数据工程将得到广泛采用,基于事件驱动、自动扩展和按需付费的特性,能够有效降低成本并提高效率。同时,AI技术将在数据工程中扮演越来越重要的角色,从数据摄取到数据清洗,再到智能检测,AI都将大大提高数据处理的速度和准确性。 此外,文章强调了模块化、可重组的数据软件系统(即“可组合”)的重要性,这种系统像乐高积木一样,可以根据需要灵活组合不同的模块,从而构建出更加灵活和高效的数据平台。 最后,文章提出了几个开放性问题,鼓励读者提出疑问,以进一步探讨数据工程的未来发展。
"数据工程的未来趋势是什么?" "如何利用AI技术提升数据工程效率?" "如何构建可插拔式的数据平台架构?"
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