Data Warehouses vs Data Marts: Architecture, Applications, and Use

This essay provides a comparative analysis of data warehouses and data marts, defining each concept and outlining their key differences. A data mart is presented as a subject-oriented subset of a data warehouse, designed to serve specific user groups, while a data warehouse is described as a centralized repository for an organization's historical data used for decision-making. The essay details the applications of data warehousing in enhancing operational efficiency, avoiding past challenges, and facilitating data integration. Similarly, it discusses the applications of data marts in creating static copies of fact tables, building data mining models, and easing query processes. The essay references several academic sources to support its analysis and definitions.

DATA WAREHOUSES VS DATA MARTS 1
DATA WAREHOUSES vs DATA MARTS.
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DATA WAREHOUSES VS DATA MARTS 2
Data mart vs. data warehouse
Data mart is defined as a subject of the data warehouse. It is also known as a storehouse
that is meant to serve certain community as well as to meet the needs of a certain group of users.
On the other hand, Data warehousing is defined as a big central storing place for all the
companies’ data. The data kept in this repository is usually historical and stored from a historical
point of view ((Malhotra, 2015).Unlike data mart, data warehouse is a collection that is separate
from the operational systems. The data in this place is used for decision-making purposes.
Summary of the differences.
DATA WAREHOUSE DATA MART
Holds multiple objects. Only one subject area can be accommodated such as
finance.
It works on the integrated data. Is focus is on integrated from a given subject area or
even a set of source systems.
Des not use dimensional approach Finally, the system is built with the focus of a
dimensional model and uses the start scheme.
The detailed stored in the data warehouse is more
detailed.
Not so much detailed.

DATA WAREHOUSES VS DATA MARTS 3
Application of a data warehousing.
Data warehouse is used in organizations to make quick decision. This enhances the operational
efficiency of the organization.
Secondly, through the use of data warehouse, organizations will avoid challenges that they may
have faced in the past. Data warehouse provides a quick solution based on the history of the
company due to this, the organization will have less trouble making certain decision ((Jukic &
Vrbsky, 2016). this application is justified because it saves the organization’s time.
Finally, Data warehousing is also used in data integration. This is important because a
fast decision can be arrived at and members of other departments can get it in a very short time.
Application of data mart.
Data marts are applied in creating static copy of a few fact tables. This table is extracted
from the main data warehouse when needed.
Data marts are used to create data mining model. This makes data marts more important
because it reduces the amount of workload that may be experienced in the data warehouse.
Data marts are used when the database manager wants to ease the query in the
warehouses. With a single data marts, one is able to query one particular table.

DATA WAREHOUSES VS DATA MARTS 4
References.
Jukic, N., Vrbsky, S. and Nestorov, S., 2016. Database systems: Introduction to databases and
data warehouses. Prospect Press.
Jukić, N., Sharma, A., Nestorov, S. and Jukić, B., 2015. Augmenting data warehouses with big
data. Information Systems Management, 32(3), pp.200-209.
Malhotra, N., 2015. Implementation of Data Marts in Data ware house. International Journal of
Advance research, Ideas and Innovations in Technology, 1(12).
Prakash, D. and Prakash, N., 2017, May. Requirements driven approach to data warehouse
consolidation. In Research Challenges in Information Science (RCIS), 2017 11th
International Conference on (pp. 449-450). IEEE.