超越数据网格幻象：设计现代AI增强型湖仓以弥合理论与实践差距
Beyond the Data Mesh Illusion: Designing Modern AI-augmented Lakehouses to Bridge the Gap Between Theory and Practice
AI总结：
针对企业数据平台中领域自服务与整体治理之间的张力，提出一种基于现代湖仓架构的AI增强型中心辐射模型，通过中心卓越中心提供共享服务与AI治理，领域团队逐步承担更多责任，以平衡灵活性与控制，并通过数据产品采纳率、查找时间和洞察时间三个指标评估架构效果。
摘要：
企业数据平台面临着领域自服务与整体治理之间的持久张力。数据网格范式提出了去中心化的领域所有权作为解决方案，但纯粹的实现往往效果不佳：团队在没有足够的平台成熟度、工具或协调机制的情况下继承了新的责任。本文认为，通过在现代湖仓架构上叠加AI增强的中心辐射模型，可以缓解灵活性与控制之间的权衡。中心枢纽（卓越中心）提供共享平台服务、策略自动化和AI驱动的治理，自动标准化数据产品、生成质量规则、起草数据合约并审查变更以检测回归。领域辐条拥有业务语义、产品积压和本地迭代节奏，随着成熟度提高逐步承担更多责任。执行治理任务的同一LLM也降低了领域从业者发展跨业务和数据工程的真正跨职能专业知识的门槛，使辐条团队能够承担更大的端到端所有权，而无需按比例增加对中心的依赖。自然语言对话界面进一步为业务用户民主化访问，释放了历史上未充分利用的企业数据。在组织方面，我们提出了一个分阶段框架，将所有权从中心转移到辐条，避免了集中式瓶颈和不协调的去中心化。我们通过三个结果指标评估架构：数据产品采纳率、查找时间和洞察时间，这些指标将平台成功与可衡量的业务价值而非内部活动联系起来。
英文摘要：
Enterprise data platforms face an enduring tension between domain self-service and holistic governance. The data mesh paradigm proposed decentralized domain ownership as a remedy, but pure implementations frequently underdeliver: teams inherit new responsibilities without the platform maturity, tooling, or coordination mechanisms needed to exercise them effectively. This paper argues that the flexibility-versus-control trade-off can be relaxed through an AI-augmented hub-and-spoke model layered on a modern lakehouse architecture. A central hub (Center of Excellence) provides shared platform services, policy automation, and AI-enabled governance, automatically standardizing data products, generating quality rules, drafting data contracts, and reviewing changes for regressions. Domain spokes own business semantics, product backlogs, and local iteration cadence, progressively assuming greater responsibility as they mature. The same LLMs that automate governance tasks also lower the barrier for domain practitioners to develop genuine cross-functional expertise spanning business and data engineering, enabling spoke teams to take on greater end-to-end ownership without proportionally increasing their dependence on the hub. Natural-language conversational interfaces further democratize access for business users, exposing historically underutilized enterprise data. On the organizational side, we propose a staged framework that shifts ownership from hub to spokes, avoiding both centralized bottlenecks and uncoordinated decentralization. We evaluate the architecture through three outcome metrics: data product adoption, time-to-find, and time-to-insight, that tie platform success to measurable business value rather than internal activity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
