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.

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.

In Part 1, we built a contextual bandit prototype using Python and Mab2Rec. While effective for testing algorithms locally, a monolithic script cannot handle production scale. Real-world recommendation systems require low-latency inference for users and high-throughput training for model updates.

This post demonstrates how to decouple these concerns using an event-driven architecture with Apache Flink, Kafka, and Redis.

Traditional recommendation systems often struggle with cold-start users and with incorporating immediate contextual signals. In contrast, Contextual Multi-Armed Bandits, or CMAB, learn continuously in an online setting by balancing exploration and exploitation using real-time context. In Part 1, we develop a Python prototype that simulates user behavior and validates the algorithm, establishing a foundation for scalable, real-time recommendation systems.

The standard architecture for modern web applications involves a decoupled frontend, typically built with a JavaScript framework, and a backend API. This pattern is powerful but introduces complexity in managing two separate codebases, development environments, and the API contract between them.

This article explores an alternative approach: an integrated architecture where the backend API and the frontend UI are served from a single, cohesive Python application.

In this post, we develop a real-time monitoring dashboard using Streamlit, an open-source Python framework that allows data scientists and AI/ML engineers to create interactive data apps. The app connects to the WebSocket server we developed in Part 1 and continuously fetches data to visualize key metrics such as order counts, sales data, and revenue by traffic source and country. With interactive bar charts and dynamic metrics, users can monitor sales trends and other important business KPIs in real-time.