Infrastructure is critical for the efficient operation of Local Digital Twins (LDTs) by supporting both governance commitments and technical needs. Effective data flows, whether real-time, continuous, batch, or event-driven, require the right infrastructure to meet specific demands such as low latency, bandwidth, reliability, and data processing location. Successful infrastructure enables actionable insights from raw sensor data. Choosing the right infrastructure involves tailoring it to the specific data flow type. Real-time scenarios require low-latency networks and edge processing, while batch processing can leverage scheduled uploads and centralized servers. Adopting edge, cloud, or hybrid architectures impacts factors like latency, resilience, and compliance. The correct architecture ensures efficient flow and supports governance goals. This unit teaches stakeholders to design infrastructure aligned with governance and flow needs, focusing on real-time, continuous, batch, and event-based data flows. Participants explore processing architectures like edge, cloud, and hybrid models, considering cost and regulatory compliance. Activities guide them in mapping flow scenarios to network strategies, encompassing redundancy and resilience in design planning.
T4R - Learning journey
Learning with Microlearning Units
Data Sharing Infrastructure
FRAMEWORK:
TECHNICAL DESIGN
MODULE:
Data Flows in Digital Twins
EQF 5
TEC-203
| TEC-200 | second loop |
|---|---|
| Data Sharing Infrastructure | Learner describes infrastructure requirements for LDTs, including edge, cloud, hybrid processing and wired or wireless networks. Learner assesses which Internet of Things, processing and storage options match data flows, resilience needs and governance or EU compliance constraints. Learner independently designs and justifies infrastructure choices for data sharing in city digital twin projects. |
