Real-time data flows are crucial for enhancing Local Digital Twins (LDTs), which risk remaining static and ineffective without them. These flows 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 traffic control. The proposed solution involves integrating Internet of Things (IoT) sensors and machine learning to facilitate real-time data flows. This approach ensures that data is continuously processed and actionable insights are generated without delay. Selecting the appropriate data flow type—batch, near-real-time, or true real-time—enhances operational effectiveness based on specific scenarios and governance needs. The learning unit covers how to implement real-time data flows in LDTs by comparing different data flow 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 flow types and protocols, ensuring compliance and efficiency in real-time scenarios.