Knowledge within engineering organizations is often scattered across repositories, pull requests, issue trackers, CI/CD systems, and documentation. While each system contains valuable information, understanding the relationships between people, projects, services, and decisions remains a significant challenge.

In this talk, I'll demonstrate how to build a production-ready knowledge graph platform using Python and event-driven architecture principles. We'll explore how streams of events—such as Git commits, pull requests, code reviews, deployments, and tickets—can be transformed into entities and relationships that continuously evolve a graph representation of organizational knowledge.

The session will walk through the complete architecture, including event ingestion, stream processing, graph modeling, incremental updates, and query patterns. We'll discuss practical design decisions, scalability considerations, and lessons learned from building systems that process large volumes of events while maintaining an accurate and up-to-date graph.

Topics covered include:
• Designing event-driven pipelines with Python
• Modeling entities and relationships for knowledge graphs
• Processing real-time events using message brokers
• Incremental graph updates and consistency challenges
• Querying relationships across people, projects, and systems
• Using knowledge graphs to enhance search, recommendations, and AI applications
• Production lessons learned from building graph-powered systems

Attendees will leave with a practical understanding of how Python can be used to build scalable knowledge graph platforms and how graph-based approaches can unlock insights that are difficult to discover using traditional databases alone.