Differences between data warehouses and data marts.

Question

Data Warehouses and Data Marts

Discusses the differences between data warehouses and data marts.

Also, discuss how organizations can use data warehouses and data marts to acquire data.

Sample paper

Differences between data warehouses and data marts

In recent times, data and information have become increasingly part of the human being as he seeks to use it to perform various daily activities such as education and training. A data mart is a subset of a warehouse and is often oriented to a particular business line or a group.  On the other hand, a data warehouse is a storage to protect and save information from different sources (Inmon & Lindstedt, 2015).  Despite being closely related, data mart and data warehousing have significant differences as the following points show.

  1. Data scope – a data warehouse helps a user to store all kinds of data in relation to the system while a data mart can only allow the user to store data concerning a specific subject. Therefore, a data warehouse has a large data scope and is in general in nature while a data mart has a smaller scope and it is much more focused.
  2. Size – based on the definition, it is clear that a data warehouse is far much bigger in size compared to a data mart. As a matter of fact, a data warehouse is made up of several data marts. A data warehouse can store large and diverse quantities of data.
  3. Integration – a data warehouse integrates different sources of data for it to feed its databases and the system needs since it stored different genres of data. However, a data mart is less integrated as it houses on a specific data with regard to a specific phenomenon (Linstedt & Olschimke, 2015).
  4. Management – given its size and integrations, the management of a data warehouse is far much complex compared to a data mart. Since data marts are smaller and subject oriented, they are easy to manage

References

Inmon, W. H., & Lindstedt, D. (2015). Data architecture: A primer for the data scientist : big data, data warehouse and data vault. Waltham, MA: Morgan Kaufmann.

Linstedt, D., & Olschimke, M. (2015). Building a scalable data warehouse with data vault 2.0.

Related:

Cost benefits to cloud computing

Leave a Reply

Your email address will not be published. Required fields are marked *