Dynamic DES v0.8.1 introduces native Data Lake integration. Learn how to use a single SimPy codebase to generate batch Parquet data for ML training, and seamlessly transition to streaming live Kafka events for production inference.

In Part 2 of our series, we dive into the code and architecture of dynamic-des. Learn how to use the Switchboard pattern, mutable resources, and dynamic topic routing to transform a static model into a synchronized forecasting engine.

Many systems marketed as digital twins exist in an ambiguous middle ground. We look at the architectural layers separating traditional simulations, operational twins, and event-driven hybrid pipelines.

Discover how to build a fault-tolerant streaming architecture using Apache Flink and Kotlin. This guide demonstrates applying Online Machine Learning to autonomously detect concept drift and correct for physical machinery wear in real-time, which is safely managed by a deterministic Shadow Mode router.