Since the v0.8.1 release, Dynamic DES has gained a declarative SimulationContext API, native Postgres and Redis ingress and egress connectors, and broader object storage support. This post walks through what changed and why the new builder pattern makes real-time digital twins far easier to write.

Provisioning a local data platform usually means fighting dependency conflicts, port clashes, and brittle Docker Compose files. odctl is a small CLI that turns Kafka, Flink, Spark, Trino, Iceberg, Airflow, and a full MLOps and observability suite into a cohesive stack you can launch with a single command. Now available on PyPI.

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.