Data EngineeringJanuary 18, 202611 min read

Lakehouse Reference Architecture for AI-Ready Data

A modern lakehouse can unify BI and ML workloads when storage, metadata, governance, and compute are designed as one platform.

Separate storage from compute deliberately

Elastic compute unlocks cost control and workload isolation, but only when governance and metadata are centralized. Without this, teams recreate silos in a new stack.

Design for multi-modal data products

AI workloads need structured tables, event streams, documents, and embeddings to coexist. The platform should support each data type with shared lineage and access controls.

  • Bronze/Silver/Gold data product contracts
  • Versioned schema and metadata enforcement
  • Unified catalog for analytics and ML discovery

Operationalize with platform SLOs

Data platforms should be managed like products. Define SLOs for freshness, quality, and availability, then automate alerts and incident response around them.

This is how lakehouses become enterprise infrastructure instead of pilot environments.

Build a data platform that scales

Kyper designs lakehouse foundations for analytics, BI, and AI workloads.