Kimball vs Inmon vs Data Vault
The three classic warehouse design approaches, what each optimises for, and how modern teams combine them.
Data WarehousingIntermediate2 min readUpdated 11 Oct 2026
Three approaches
| Kimball | Inmon | Data Vault | |
|---|---|---|---|
| Core idea | Dimensional models per business process | Normalized enterprise warehouse first, marts after | Hubs, links and satellites that record history |
| Delivery | Fast, incremental | Slower, centralised | Flexible integration layer |
| Strength | Easy for analysts and BI | Single integrated source of truth | Auditable, handles changing sources |
| Weakness | Conformance takes discipline | Big upfront design | Not query-friendly; needs marts on top |
Kimball in one picture
dim_date dim_store
\ /
fact_sales — one row per order line
/ \
dim_product dim_customerConformed dimensions (the same dim_customer everywhere) let facts from different processes be compared.
Data Vault in one picture
hub_customer (business key) ── link_order_customer ── hub_order
| |
sat_customer_details (history) sat_order_status (history)Hubs hold keys, links hold relationships, satellites hold descriptive attributes with load timestamps. New sources add satellites without changing existing tables.
What teams do today
A common lakehouse stack mixes them: a raw/bronze layer, an integrated and historised layer (sometimes Data Vault), and Kimball-style marts for consumption.
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