Part of the Analytify BI Glossary, clear definitions of the business intelligence, analytics, and modern data stack terms that matter.
A data mart is a focused, subject-specific subset of a data warehouse, designed to serve the analytical needs of a single department, business unit, or use case — for example, a marketing data mart, a finance data mart, or a customer success data mart.
Data marts emerged in the 1990s as a pragmatic answer to two problems with enterprise data warehouses: queries on the full warehouse were slow, and different departments had different definitions of the same metrics. Each department got its own data mart, optimised for their queries with their definitions.
In 2026, the rise of cloud data warehouses with elastic compute has reduced the technical need for separate data marts. But the organisational and modelling concept persists — modern dbt projects organise their final layer into “marts” by domain (orders mart, marketing mart, finance mart) for governance and clarity.
A data mart typically:
The 2026 alternative to data marts is the “single warehouse with marts as schemas” pattern — one physical warehouse, with logical separation into marts via schema-based dbt project structure.
A SaaS company’s dbt project organises models into three marts: marts/finance/ (revenue facts, customer dimension, contract dimension), marts/marketing/ (campaigns, attribution, lead scoring), marts/product/ (events, feature usage, retention cohorts). Each mart has its own owning team, its own metric definitions, and its own access controls. The same warehouse hosts all three marts but they remain logically separated.
2026 data mart implementation tools:
Industry-standard tool for building data marts as logical layers inside a warehouse.
Cloud warehouses where data marts live as schemas or databases.
Semantic layers that expose data marts to BI consumers.
Code-based BI with strong data mart modelling support.
Open-source GenBI platform that reads data marts for SaaS embedded analytics.
A data warehouse serves the entire organisation across all subject areas. A data mart focuses on one subject area for one department. In modern stacks, data marts are typically logical schemas within a single warehouse.
Yes, as a logical organising principle. Modern dbt projects use the marts pattern to organise final-layer models by domain. The “physical separate databases” form of data mart has largely been replaced by single-warehouse architectures.
A dependent data mart sources from a central data warehouse — single source of truth. An independent data mart pulls directly from operational sources, bypassing the warehouse — leads to data silos and duplicated logic. Dependent is preferred.
Define the subject area and stakeholders. Identify required source data. Build dbt models in a marts schema (e.g. marts/finance/). Apply data tests. Expose via the semantic layer or BI tool. Each mart should have a clear owner and documented definitions.
In modern data mesh thinking, a data product is a self-contained data asset with its own owners, SLA, and consumers. A data mart can be one form of a data product when it serves a specific domain.
Yes. Star schema is the standard data mart modelling pattern — central fact tables surrounded by shared dimensions. Modern dbt marts almost universally follow this pattern.