Building Real-Time Intelligence Systems in Production
KGC 2026
•
17m
https://drive.google.com/file/d/1GqheySdjCqY7N0bYpxm1bz495V1KYG1_/view?usp=share_link
Event-driven systems generate rich signals, but without context they remain fragmented and hard to reason over. Knowledge graphs offer a powerful way to connect these signals — yet building and operating them in real-time production environments presents unique challenges.
In this talk, I’ll walk through a practical, end-to-end architecture for constructing real-time knowledge graphs from streaming systems such as Kafka. Using real-world enterprise use cases from fraud detection and identity intelligence, I’ll cover how events are transformed into graph entities and relationships, how schemas evolve over time, and how to balance low-latency ingestion with query performance.
The session will dive into:
Designing graph schemas for high-volume, streaming data
Handling schema evolution, late-arriving events, and data quality issues
Trade-offs between real-time and batch graph updates
Query patterns that worked in production — and those that failed
Observability strategies for monitoring graph health, freshness, and performance
How knowledge graphs complement ML models and rule engines to enable explainable AI decisions
Attendees will leave with concrete design patterns, architectural trade-offs, and hard-earned lessons for deploying and operating knowledge graphs in production-scale systems.
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