10/12/2024
Extending Microsoft’s Azure Digital Twins for real-time analytics
As countless applications need to track live systems, developers face the challenge of implementing real-time analytics that can react to incoming telemetry and quickly identify problems or opportunities. Examples include telematics software that tracks vehicles in a fleet, security software monitoring physical points of entry or network endpoints in a cyber infrastructure, health-tracking systems that analyze telemetry from wearable devices, and many others. These applications are all tasked with the need to simultaneously digest messages from numerous data sources, find patterns of interest, and act quickly when necessary.
Using Digital Twins for Real-Time Analytics
The digital twin model has evolved over the last twenty years as a compelling, object-oriented approach to modelling the state and behavior of devices, and it has been widely adopted for product lifecycle management (PLM). Using the power of in-memory computing, new capabilities for real-time analytics can now be added to the digital twin model and enable it to track telemetry from large numbers of data sources. This approach has the potential to simplify application design and streamline the development process while enabling consistently fast analysis even for large workloads. It also can easily be integrated into popular digital twin platforms, such as Microsoft’s Azure Digital Twins, to expand their range of applications from PLM to real-time analytics for live systems with many data sources.
