Data warehouses/data marts are closely related concepts of data structuring, but there exist a few differences between them. One of the differences is that a data warehouse is used for multiple subject areas, while a data mart holds a specific subject area, for example, sales or finance (Chu, 2004; Laberge, 2011). Another difference is that data warehouses hold significantly detailed information, while data marts, on the other hand, hold summarized data. In addition, a data warehouse is used for the integration of different data sources, while a data mart is used for the integration of information related to a given subject area or source system.
Another difference between a data warehouse and a data mart is that the former feeds dimensional models, but they may not necessarily use a dimensional model while the latter focus on a dimensional model utilizing a star schema (Chu, 2004; Laberge, 2011). In the end, it is possible to regard a data mart as a specific piece of a data warehouse that has information about a specific area of business.
An organization can use a data warehouse as a facility for the management of business intelligence data. In this case, a data warehouse is used for obtaining, cleaning, organizing and cataloging data (Imhoff, Galemmo and Geiger, 2003). However, the user of a data warehouse for the functions mentioned above needs to beware of such problems as data non-integration, vast data volumes, and missing values. On the other hand, an organization can use a data mart for the purpose of collecting information regarding needs of a specific business area or department. Usually, the scope of use of a data mart is smaller compared to that of a data warehouse.
References.
Chu, M. Y. (2004). Blissful data: Wisdom and strategies for providing meaningful, useful, and accessible data for all employees. New York: AMACOM.
Imhoff, C., Galemmo, N., and Geiger, J. G. (2003). Mastering data warehouse design: Relational and dimensional techniques. Indianapolis, IN: Wiley Pub.
Laberge, R. (2011). The data warehouse mentor: Practical data warehouse and business intelligence insights. New York: McGraw-Hill.
