The lakehouse is no longer just a place to land tables. In an AI-native enterprise, it must serve structured analytics, vector search, and feature retrieval from a single governed substrate.
Why the Old Stack Breaks
Most "AI platforms" are stitched together from three disconnected systems:
- A warehouse for BI
- A vector DB for RAG
- A feature store for ML
Each gets its own copy of the data, its own access policy, its own lineage gap. The result is duplication, drift, and a security surface that no auditor can reason about.
The CYBERIA Pattern
We collapse the three into one open-table foundation:
- Iceberg or Delta as the storage substrate
- Embeddings as columns — vectors live next to the rows that produced them
- Feature views materialized through dbt + a thin online cache
- Unity-style governance that treats vectors, features, and tables identically
What This Unlocks
- One ACL surface for analysts, ML engineers, and AI agents
- Lineage that follows a token from prompt → retrieval → source row
- Sub-150 ms p99 retrieval without a separate vector cluster
The AI-native lakehouse is not a product you buy. It is an architectural discipline.