As data mesh architectures gain traction in federated environments, organizations are increasingly building consumer-specific data-sharing pipelines using modular, cloud-native transformation services. Prior work has shown that structuring these pipelines with reusable transformation stages enhances both scalability and energy efficiency. However, integrating traditional cloud design patterns into such pipelines poses a challenge: predefining and embedding patterns can compromise modularity, reduce reusability, and conflict with the pipelines dynamic, consumer-driven nature. To address this, we introduce a Kubernetes-based tool that enables the deferred and non-intrusive application of selected cloud design patterns without requiring changes to service source code. The tool supports automated pattern injection and collects energy consumption metrics, allowing developers to make energy-aware decisions while preserving the flexible, composable structure of reusable data-sharing pipelines.

翻译：随着数据网格架构在联邦环境中日益普及，各组织正越来越多地使用模块化、云原生的转换服务构建面向特定消费者的数据共享管道。先前的研究表明，通过可复用的转换阶段来构建这些管道，既能提升可扩展性，又能提高能源效率。然而，将传统的云设计模式集成到此类管道中面临着一个挑战：预先定义并嵌入模式可能会损害模块性、降低可复用性，并与管道动态、消费者驱动的特性相冲突。为解决这一问题，我们提出了一种基于Kubernetes的工具，该工具能够在无需修改服务源代码的情况下，实现对选定云设计模式的延迟、无侵入式应用。该工具支持自动化模式注入，并收集能耗指标，使开发人员能够在保持可复用数据共享管道灵活、可组合结构的同时，做出能源感知的决策。