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速度与规模的架构.pdf

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1、Architecting for Speed and ScaleGet consistency and realtime latency with a Data Integration Hub(DIH)AgendaIntroductionGenerative AI is the least important AI.Concurrency matters!“Big Data”isnt a big deal.Containerization is complicated.Cloud is not king.Performance often doesnt matter.Real-time pro

2、cessing isnt what you think.ETL to ELT,now back to ETL again aka Data Integration HubTips&takeawaysIntroductionIntroduction28 years in data management software.OReilly author.IASA Architecture Fundamentals certified.Hortonworks,Syncsort,Bloor Group,Actian,Pervasive,CSC,Data Junction,GridGain,Vertica

3、.Cant seem to decide what I want to be when I grow up:Software EngineerSupport TechTech WriterSoftware TrainerConsultantProduct and Technical MarketerProduct ManagerPaige Roberts,Data Nerd Actively looking for a new position!Ive spent way too many years of my life geeking out about enterprise data a

4、rchitecture.Generative AI is the least important AI.Right now.Realms of Data ScienceData ScienceThe use of algorithms to extract knowledge and insights from data in various forms.Some subfields:Statistics,Artificial Intelligence(AI),Computational MathData ScienceArtificial IntelligenceMachine Learni

5、ngArtificial Intelligence(AI)The simulation of intelligent human behavior for problem-solving and decision-making.Some subfields:Robotics,Natural Language Processing(NLP),Machine Learning(ML)Machine Learning(ML)The process by which machines are taught to make calculated suggestions and/or prediction

6、s by examining large amounts of input dataSome subfields:Regression,Deep Learning,Reinforcement Learning,ClusteringRealms of Data ScienceData ScienceThe use of algorithms to extract knowledge and insights from data in various forms.Some subfields:Statistics,Artificial Intelligence(AI),Computational

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本文主要内容为关于数据集成中心(Data Integration Hub,DIH)的架构设计,以及如何实现速度和规模上的优化。文章首先介绍了AI的不同领域,包括生成性AI、机器学习等,并指出生成性AI目前并不是最重要的AI领域。接着,文章讨论了并发处理的重要性,以及“大数据”处理并不像人们想象中的那么复杂。此外,文章还探讨了容器化技术的复杂性,以及云服务并非万能。 文章进一步指出,性能并不总是最重要的考虑因素,实时处理也并非人们所想的那样。文章还提到了ETL(提取、转换、加载)到ELT(提取、加载、转换)再到ETL的转变,即数据集成中心的重要性。最后,文章给出了关于如何应对技术变革的建议和总结。 总的来说,文章强调了在数据集成和处理中,需要考虑并发处理、实时处理、性能与成本的平衡,以及技术的选择和应用。同时,文章也指出了数据集成中心在实现这些目标中的关键作用。
实时数据分析的重要性是什么? 数据集成中心如何提高业务效率? 为什么性能并不总是最重要的?
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