Feature store vs data warehouse
A data warehouse holds large volumes of history and answers analytical queries. A feature store serves individual feature values to a model with low latency. A warehouse is where offline features are computed; it is not where online features are served.
What a warehouse is for
A data warehouse stores large amounts of history and answers analytical queries across it. It is built for scans over millions of rows, not for returning one customer’s current features in the time a model has to respond. Computing offline features over history is exactly its job.
Where it stops
When a model runs in production, it needs one customer’s features back fast, every time it is called. A warehouse is not built for that shape of request. Querying it per prediction is too slow and too expensive, so teams put a separate serving store in front of it.
What the feature store adds
That serving store is the online half of a feature store. It keeps current feature values ready to read in milliseconds, computed from the same definitions the warehouse uses for history. The warehouse still does the heavy historical work. The feature store makes the result servable in production.
See it on your own traffic
Add the SDK, define one attribute, and see an attribute updating against your own traffic in the first session. The free tier runs on the real engine: no card, no sales call.