Data Engineering for AI-Readiness
Jonathon KindredData Engineering for AI-Readiness: The 41-Page Handbook
Architect, don’t retrofit. In under an hour, learn exactly why reporting-first data stacks stall machine-learning initiatives and the precise moves that will set yours free.
What you’ll master: fast
- From BI to AI in one diagram. See how legacy ETL and star-schema thinking throttle real-time intelligence, and map the shift to an Intelligence-First blueprint.
- The four non-negotiable principles of AI-ready platforms: modularity, separation of compute & storage, end-to-end observability, and data-as-a-product contracts.
- Field-tested patterns & anti-patterns for lakehouse, medallion, data mesh, and hybrid real-time/batch pipelines. Know when to combine them, and when to walk away.
- Action checklists for DataOps, FinOps, and responsible AI that you can plug straight into your next sprint.
“This is going to be the de facto material I use when executives ask, ‘Why can’t we just bolt AI onto the warehouse?’” — Draft Reviewer
About the author
I’m Jonathon Kindred, Principal Data Engineer. After watching Data Platform innovates and AI projects grind to a halt on reporting-centric platforms, I distilled the hard-won lessons into this short handbook. My mission: help you pivot from reporting-first to intelligence-first without a rip-and-replace rewrite.
Stop retrofitting yesterday’s architecture. Grab the handbook and start building AI-ready foundations today.