What is a data mart?

A data mart is a subset of the data warehouse scoped to a single department or domain (sales, marketing, finance), with fact and dimension tables shaped around that team's questions. Where the enterprise warehouse covers the whole organization, the mart carves out one slice of it: less data, fewer joins, faster answers for the people using it every day. The model exists as a practical answer to a recurring problem in large organizations: the sales team does not need to query the entire enterprise warehouse to know what it sold yesterday, it needs a small, fast schema built around its own specific metrics. Historically the mart was a separate physical database, fed by its own ETL process; in modern architectures it is more often a view or a set of tables built on top of the same shared layer as the central warehouse, which reduces duplication but does not remove the risk of diverging definitions across marts.

See also: Star schema · A dimensional model with a central fact table linked to dimension tables: the schema underneath most BI dashboards.

Fast to build, risky to multiply

The mart's advantage is speed: a team that would wait months for a full enterprise view can have its own mart in weeks, with a star schema dedicated to its metrics. The risk shows up when marts multiply without a shared model behind them: every department defines "active customer" or "net revenue" its own way, and the numbers stop reconciling across marts. That is the historical debate between Kimball's approach, where conformed dimensions are designed first and marts are delivered one at a time, and Inmon's top-down approach, where the central warehouse comes first and marts descend from it. In modern practice, with data lakehouses and versioned SQL transformation, a mart is often no longer a separate physical database but a view or a dbt model built on top of the same shared gold layer.

Why it matters for your business

A well-governed data mart gives every team its fast view without sacrificing company-wide consistency, as long as the key dimensions (customer, product, time) stay the same definitions used everywhere. The warning sign to watch for is disconnected marts proliferating, each with its own calculation logic: before that happens it pays to decide who owns the shared dimensional model and who decides when a new mart is needed, instead of letting one spring up every time a department asks for a faster dashboard.