Local Digital Twins (LDTs) are vital for cities aiming to enable evidence-based decision-making, yet confusion persists about deploying them effectively at scale. This unit leverages real- world examples, especially the DUET project, to demystify LDT architectures, offering clarity through practical demonstration. The unit presents DUET as a model system integrating urban data streams into a real-time decision-support environment. By evaluating cases like DUET, Málaga, and others, learners can grasp how these systems employ bidirectional data fl ow and predictive analytics for dynamic policy testing, demonstrably exceeding static digital models. By examining DUET and other advanced cases through foundational frameworks (TEC-101 to TEC-105), this learning unit teaches participants to understand and assess LDT maturity and design quality. It highlights architectural layers, software principles, and interoperability standards, enabling learners to evaluate or replicate similar systems.