Digital Twins (DTs) are virtual representations of physical assets characterized by bidirectional data exchange, enabling advanced services such as monitoring, prediction, control and optimization. Despite growing interest in DTs, their adoption remains hindered by fragmented architectures and limited interoperability. To address these challenges, we introduce a DT-enabled Ecosystem (DTE) architecture grounded in a multi-view modeling framework, inspired by Kruchten’s view model. The proposed architecture includes logical, technological, and development views, leveraging the FIWARE open-source platform to ensure scalability, data interoperability, and real-world applicability. The approach is validated through a smart urban mobility case study using open-source Eclipse SUMO for traffic simulations. Ongoing advancements regard the adoption of the architecture in model-driven engineering approach and extend the proposal with EdgeAI capabilities, enabling privacy-aware, low-latency, and scalable analytics at the network edge.