Real-time data fl ows are crucial for enhancing Local Digital Twins (LDTs), which risk remaining static and ineffective without them. These fl ows enable LDTs to transition to a dynamic, decision-support tool, critical for timely responses in urban scenarios. Real-time capability supports informed, immediate interventions in areas such as emergency management and traffi c control. The proposed solution involves integrating Internet of Things (IoT) sensors and machine learning to facilitate real-time data fl ows. This approach ensures that data is continuously processed and actionable insights are generated without delay. Selecting the appropriate data fl ow type—batch, near-real-time, or true real-time—enhances operational effectiveness based on specifi c scenarios and governance needs. The learning unit covers how to implement real-time data fl ows in LDTs by comparing different data fl ow types and their applicability. It addresses architectural patterns and communication protocols, and evaluates solutions for overcoming latency issues. This unit equips learners with the framework to manage data fl ow types and protocols, ensuring compliance and effi ciency in real-time scenarios.