Summary

The article discusses the evolving role of data engineers in the context of Agentic AI, highlighting how the interaction with data is shifting from a builder-centric model to a more user-driven approach. It emphasizes the importance of rethinking traditional ETL/ELT processes, prioritizing data curation over mere collection, and building infrastructure that supports AI agents. The author outlines five key principles for data engineers to adapt to these changes, focusing on context-aware data handling and the management of AI-generated artifacts.

💭 Your Thoughts

I think building a semantic layer on top of your data for GenAI is one good pattern.

Data Problems Addressed