Data Warehouse vs Data Mart: Definition and Differences

Databases are part of the basic toolkit of any organization. However, usually only data managers are aware of the differences between the many types of databases that exist. In this article we explain the basic differences between a data warehouse and a data mart for non-experts.

data warehouse y data mart definiciones y diferencias

Nowadays it is difficult to find someone who does not know what a database is. Databases are now to the business world what digital whiteboards are to the educational world if they had completely replaced typical chalk boards. However, it is quite normal that within an organization, only technicians, data analysts and data scientists know the differences between the different types of databases and their purposes.

In this blog we have previously discussed the differences between a data warehouse and other databases, as well as the difference between a data warehouse and a data lake. Thus, we have covered the most common databases in the business world, although it is essential to explore data marts, their functions and differences with a data warehouse.


What Is a Data Warehouse?

As the name implies, a data warehouse is indeed a database for data integration. In fact, it usually serves as the central database of a company or, in other words, the database where all the useful data of an organization is stocked.

The peculiarity of the data warehouse is that it is structured to promote the comprehension and organization of the data, which is precisely why it is so widely used in the business world. However, it is not an operational database. In other words, the data integrated into a data warehouse is not intended to be used imminently, but it is stored for future use.

The data stored in a data warehouse comes from multiple sources and in countless formats. Once stored in the data warehouse, it can be organized by topics according to the data consumer's preference and the information is structured into views, processes, dimensions and metrics —usually in star or snowflake schemas—. In addition, a data warehouse has the ability to answer complex queries which resolve doubts about the business activity and serve as a guide when defining strategies and making decisions.  

Data warehouses are designed to facilitate data analysis, as they can be integrated with business intelligence tools such as Power BI. At Bismart, as a Microsoft Power BI partner, we work with this tool to offer the best technical capabilities to our customers.

A data warehouse, therefore, is a data store that because of its characteristics converts data assets into understandable, organized, updated and ready-to-be-analyzed information.


What Is a Data Mart?

A data mart is a subset of a database  —usually a data warehouse— where data is stored for a specific business area. That is, a data mart stores concise and specific data sets used for analysis for a specific department or line of business, such as the sales department.

The data mart is query-oriented and, as in a data warehouse, the data has a clear structure —also typically star or snowflake dimensional models—. The intent of using a data mart is to index data and facilitate queries about specific areas of the business and to meet the needs of a particular group of users within the organization. 


Data Warehouse vs Data Mart: Differences 

The main difference between the two databases is their size and approach. While a data warehouse serves as the global database of a business and stores data about any aspect of the company, a data mart stores a small amount of data related to a specific business department or project. Similarly, a data warehouse collects data from a wide variety of sources, whereas a data mart typically collects data from the central database, the data warehouse. This, in turn, means that a data warehouse has a much larger storage capacity than a data mart and a much more complex and difficult to design architecture. In addition, the implementation process of a data warehouse is more complex and time-consuming —it usually takes several months or even years— while that of a data mart can be solved in a few months since it gathers a much smaller amount of data and it has a simpler structure.

Carrying on with the example of the educational system, we could say that a data warehouse is the place where all the documents of an educational center are stored, while a data mart would be the place where each teacher or group of teachers keep the documentation relevant to their subject. 

Below, we explore in more detail the main distinctions between a data warehouse and a data mart:


In short, a data warehouse is a central database with the ability to connect to virtually any data source and with large storage capacities. A data mart, on the other hand, is a sub-area of a data warehouse, with reduced storage capacity and oriented to solve the doubts of data consumers regarding a specific area of the business.