A data warehouse is a relational database designed for analytical rather than transactional work, capable of processing and transforming data sets from multiple sources. On the other hand, a data mart is typically limited to holding warehouse data for a single purpose, such as serving the needs of a single line of business or company department.
A data mart is a curated subset of data often generated for analytics and business intelligence users. Data marts are often created as a repository of pertinent information for a subgroup of workers or a particular use case.
What’s the Difference between a Data Mart and a Data Warehouse?
As a data mart is a subset of a data warehouse, businesses may use data marts to provide user access to those who cannot otherwise access data. Data marts may also be less expensive for storage and faster for analysis given their smaller and specialized designs.
Other differences between a data mart and a data warehouse:
Size:a data mart is typically less than 100 GB; a data warehouse is typically larger than 100 GB and often a terabyte or more.
>Range: a data mart is limited to a single focus for one line of business; a data warehouse is typically enterprise-wide and ranges across multiple areas.
Sources: a data mart includes data from just a few sources; a data warehouse stores data from multiple sources.
Data Warehouse versus Data Mart
Slow and overloaded data warehouses are often the underlying reason for creating data marts and frequently serve as their underlying data source. Often, as data volumes and analytics use cases increase, organizations cannot serve every analytics use case without degrading the performance of their data warehouse, so they export a subset of data to the mart for analytics.
Snowflake: Eliminate the Need for Data Marts
Snowflake’s highly elastic, innovative cloud data architecture ensures that it can support an unlimited amount of data and users. Additional compute resources can be spun up quickly to address new use cases without affecting the other operations that are happening on the database, thus eliminating the need to spin off separate physical data marts to maintain acceptable performance.