How to connect semantic models, metrics layers, and LLMs to deliver AI experiences that are grounded in governed, trusted data.

Explains how event streams, message queues, and real-time processing power both operational analytics and context-rich AI agents.

Argues that data engineering must embrace software engineering discipline—testing, CI/CD, observability, and design patterns—to stay effective in the age of AI.