A data warehouse is a large centralized repository of data that contains information including historical data from many sources within an organization.

A data mart is a subset of a data warehouse that is focused on a single subject or line of business, such as finance, sales, or marketing.


Data mart VS Data warehouse

Data Mart

Data Warehouse

In a data mart, the data collected for analysis is based only on one single subject area. For example finance or sales.

In a data warehouse, the data collected for analysis is based on multiple subject areas.

Data mart captures data from a data warehouse or operational systems or external sources.

The data warehouse captures data from multiple data sources.

In data mart, data can be normalized.

In data warehouse, data is highly denormalized.

Data mart occupies less storage area(less than 100GB).

The Data warehouse occupies more storage area(minimum 100GB).

A data mart can be developed between 6-8 months of duration.

Data warehouse takes 1-2 years for developing completely.

Maintenance overhead is less in data mart.

Maintenance overhead is more.

The main objective of a data mart is to store and use data by a specific user community.

The main objective of a data warehouse is to support business intelligence, batch reporting, and data visualization.

In the case of a data mart, a repository of data is designed to serve a particular community of knowledge workers. 

The Data warehouse contains a central location that stores consolidated data from multiple sources(various databases).


Here in this blog, we discussed the differences between a data mart and a data warehouse. We knew that a data mart is the subset of a data warehouse that holds more summarized data whereas a data warehouse very detailed information.

A data mart is specially built focused on a dimensional model using a star schema whereas a data warehouse doesn’t need to be built using a dimensional model but rather it feeds dimensional models.

A data mart is more concentrated on integrating information from a specified subject area that could be either finance, sales, or marketing whereas a data warehouse works to integrate all data sources that are generated from the particular organization branches and sub-branches.

Differences on:

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