当前位置:首页 > 报告详情

数据网格最佳实践 (3).pptx

上传人: 王** 编号:171086 2024-07-23 32页 2.19MB

1、Data Mesh Best PracticesAbout me20+years of data and engineering experienceArchitect,builder,speakerFocused on the maturation,and modernization of dataAWS Data HeroFounder of Data FuturesFather of 4(3 boys,1 girl).always building:welding/fabrication/machining/wiring/engineering/programmingWhy do I c

2、are about Data MeshIm a software engineer first,but my superpower is data engineeringMaturing of data engineering system architecture and cultureData MaturityAdopting Data Mesh principles can actually bring Data Engineering to similar Maturity as other well engineered software products.Event Based M

3、icroservice be better to dataAND data,be betterData MeshComposable data architecture of data products with decentralized ownership and development.https:/ course on data mesh4 principles1.Domain Driven Ownership Organization and Culture -decentralized ownership Federated DevelopmentData Organization

4、 Maturity LevelsCentral-central data team,middle man,a difficult/near impossible jobEmbedded-centrally managed engineers align/embed with product/outcome teamsFederated-central teams develop tools,product/outcome teams execute 2.Data as a ProductApply“Data Product Think”.Data is not an afterthought.

5、Data is not what comes out of the tailpipe.Data is a non-functional/functional requirement for all products REQUIRES long term thinking and organizational alignmentData Product ThinkWhat are my analytic requirements?How will i measure the success of this feature/product?What are the requirements of

6、my data,both within my domain/team,and that of my customers?How can I robustly share my data model as fully as possible?Organizational AlignmentReward system,based on MetricsExisting precedence to leveragetech debtinfrastructuredevex3.Self Service Data PlatformData should be:easy to discover and und

word格式文档无特别注明外均可编辑修改,预览文件经过压缩,下载原文更清晰!
三个皮匠报告文库所有资源均是客户上传分享,仅供网友学习交流,未经上传用户书面授权,请勿作商用。
本文主要探讨了数据工程领域的数据网格(Data Mesh)概念和实践。作者Elliott Bisnowat拥有超过20年的数据和工程经验,专注于数据成熟度和现代化。他提出,采用数据网格原则可将数据工程提升至与其他成熟软件产品相当的水平。关键点包括: 1. 社会技术挑战:通过领域驱动所有权、数据作为产品等方法实现成功。 2. 数据作为产品:数据不应是事后考虑,而应作为所有产品的非功能性需求。 3. 自助数据平台:数据应易于发现、理解和使用,以支持去中心化模型。 4. 计算治理:自动化应用政策以保持分布式系统的效率。 作者强调,虽然计算治理的自动化工具有限,但应尽最大努力应用现有工具,不要让完美阻碍进步。通过使敏捷团队成为数据生产者、投资于数据产品管理和技能提升,以及建立激励机制和度量标准,可以在单一的数据仓库或数据湖环境中实施数据网格原则。
如何实现跨域协作?" 如何将数据融入产品设计?" 如何构建高效的数据架构?"
客服
商务合作
小程序
服务号
折叠