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Five kinds of methods, improve the data of engineering practice

发表:18 May 2023

总结

客户对可用数据的需求,推动了数据工程技术的发展。数据和分析领导者可以通过促进协作,提早论证业务价值,实现发布流程自动化,将数据作为产品来管理以及消除运营负担等方式,增强数据工程实践。

Included in the Full Research

An overview of the

Main findings
  • Successful data engineering team is cross functional teams, adopting DataOps practice.

  • 相比于注重技术增强,企业机构如果注重数据工程所产生的业务价值,能更高效地确定数据交付需求的优先级。

  • For new/emerging data engineering team, test and release process need to consume a large amount of manpower.Mature team have begun to these process automation reform.

  • Many enterprise organization in the data system and operating process, greatly extended the data delivery time.

  • 虽然许多企业机构已经采用了自助式数据准备,以便业务用户探索数据,但很少能看到将工作负载交付到生产阶段所需的网络控制管理流程。

建议

Hope to improve data and analyzing the data of engineering practices (D&A) leader, should be:

  • 建立包含数据,业务和技术角色的跨职能团队,并且根据需要设置产品负责人/经理,以鼓励协作。

  • In major data show before the start of the business value of the project, the value drivers, avoiding redundant data engineering work.

  • Improve the efficiency of process, first of all, realize the automation of a single test and release process, and the various steps in coordination with the release pipeline.

  • 将单一数据交付流程分解为模块化数据产品交付,倡导数据复用。

  • 使用必要的网守协议将重复性的数据准备工作推送至生产环境,减轻公民角色的运营负担。

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