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构建弹性数据团队和可扩展解决方案:来自现场的经验教训.pdf

上传人: 芦苇 编号:651691 2025-05-01 15页 318.10KB

1、Hosted by:Welcome!The presentation will begin shortly If your badge was not scanned at the door,please use the self check-in feature in the session details in the Schedule.Building Resilient Data Teams And Scalable SolutionsLessons From The Field Of Data And Analytics EngineeringGreat Lakes Data,AI&

2、Analytics SummitApr 10,2025PresentersBrittany SpencerDirector,Analytics EngineerAAA LifeHarini RajagopalManager,Data EngineeringAAA LifeAgendaEngineering is done when Data is UseableGathering Business Requirements into Technical SpecsScalable Solutions as Opposed to One and DoneCoordination for Prod

3、 Support on the Data PipelineSelf-Serve Analytics&Single Unified Source of TruthInvestment in Data Tech InfrastructureData and Analytics Engineering Not all Data Teams are the SameTruly empowered data driven organizations require support across every stage of the data pipelineEngineering is done whe

4、n Data is UseableDelivering raw data isnt the finish line of a projectTimelines should reflect the work it takes to make it consumableRobust project plans include dependencies for data deliveryBusiness Requirements!=Technical SpecsData is a natural bridge builder across business units Bridging the g

5、ap between technical specs for implementation and business requirements is much harderFocus on simple questions to view the issue from a stakeholder perspective:What questions do you need to be able to answer at the end of this project?How are you measuring the success of this launch?How people need

6、 to see the data is just as important as how to get it to themScalable Solutions vs Build for Distant FutureDesignFor smaller incrementsBuild modular pipelines with reusable componentsTerraform,Airflow,dbt and AWSStart somewhereFocus onOn Automation and MonitoringCI/CDFalse Positives monitoringPrior

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本文主要介绍了2025年4月10日举办的大湖数据、人工智能与分析峰会的内容。峰会由Harini Rajagopal和Brittany Spencer主讲,他们分别是AAA Life的Data Engineering经理和Analytics Engineer总监。会议内容包括: 1. 数据工程的目标是使数据可用,而不仅仅是提供原始数据。 2. 在项目计划中应包括数据交付的依赖关系。 3. 数据是连接各个业务单元的自然桥梁,但将技术规格和业务需求相结合是困难的。 4. 应关注简单问题,从利益相关者的角度审视问题。 5. 应构建可扩展的解决方案,而不是为未来遥远的需求而建。 6. 需要关注自动化和监控,如CI/CD和假阳性监控。 7. 应确保数据完整性和治理,建立健壮的数据质量检查。 8. 需要跨团队协调,以保持数据生产线的稳定。 9. 在自助分析中,一个共享的、统一多个源系统的真理层至关重要。 10. 应投资于数据技术基础设施,采用工具进行能力建设和信任增长。 本文强调了数据团队在企业中的重要性,以及他们在数据管道各个阶段的作用。同时,提出了确保数据可用性、可扩展性和治理的关键点,以支持企业的自助分析和数据驱动决策。
如何构建弹性的数据团队和可扩展的解决方案? 数据工程何时才算完成?数据可用性是关键吗? 如何有效管理无限的需求Backlogs并确保数据团队的成功?
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