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 a 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.||A data warehouse takes 1-2 years for developing completely.|
|Maintenance overhead is less in the 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 and 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.