Lakehouse vs. Data Warehouse: What Actually Changes

The short version

A Lakehouse combines the flexibility of a data lake (open file formats, ML-ready) with the reliability guarantees of a warehouse (ACID transactions, schema enforcement). You stop maintaining two parallel systems for BI and data science.

When it's worth migrating

If your ML and data science teams maintain a separate copy of warehouse data because the warehouse doesn't support their workloads well, that duplication — and the drift it causes — is usually the strongest signal a Lakehouse migration will pay off.

What doesn't change

Your BI tools still connect the same way. Your governance model still applies. The migration changes the storage and compute layer, not how business users consume dashboards.

What to plan for

Budget time for a parallel-run validation period before cutting over any single source system — the risk in a Lakehouse migration is rarely the technology, it's trusting new numbers before they've been proven against the old ones.