Relational Database vs. Data Warehouse

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A database refers to a collection or cluster of data that is organized for storage, retrieval and accessibility. A data warehouse, on the other hand, is a specific type of database which integrates transaction data from different sources and provides them for analytical purposes. There are distinct variations in the structures o a database that has been optimized for online transactions, and a data warehouse that has been optimized for the processing of large chunk of data. The first difference is evident the optimization of each; a relational database is optimized to facilitate read and write operations of single-instance transactions .In essence, a relational database such as the OLTP should provide response within sub-second times. A data warehouse, on the contrary, is optimized for the efficient reading, retrievals and aggregation of large chunks of data (Velicanu & Matei, 2007).

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Secondly, there is a significant difference in the organization of data. The structure of an OLTP database is composed of complex tables that join because of data normalization In essence; data is made relational hence delivering processing and storage efficiencies. With a data warehouse, data is de-normalized to allow for improved response times for analytical queries and to offer ease of use. The structure of a data warehouse is considerably simple; there are few tables, hence easier analysis and reporting.

Database requirements for operational data and decision support data

Operational data represent, or are, the daily operations of an organization, while decision support data facilitate the processing and analysis of data, they are the tools designed for data processing and analysis. Operational and decision support data differ in their granularity, dimensionality and time span. With respect to granularity, operational data are atomic-detailed, represent the transactions, while decision support data are summarized, is an instance of the operational data at a specific time. In relation to dimensionality, operational data focus on the transactions individually, while decision support date concentrates on the influences of the transaction over a period (Rob, Coronel & Morris, 2013). Lastly operational data have a current time span, it covers present operations and real-time information; while decision support date deal with historic information. As a result, most operational data are stored in a social database that has standardized tables or structures as opposed to decision support data that are stored in simple structured tables. Stockpiling of operational data should be streamlined to assist exchanges that interact with daily operations. Lastly, to offer a feasible upgrade execution, operational structures store information in several tables, with each table having a base quantity of fields (Rob, Coronel & Morris, 2013)

Use of databases to support decision-making in a large organizational environment

Databases play an important role in the decision-making process. The use of databases allows for data sharing, data is centralized allowing to be shared by various individuals and applications. Data can be easily processed without the need of creating new storage files, granting better access to data, and hence improving on decision-making process. Secondly, the use of databases brings in the aspect of accuracy and precision in the decision making process as data inconsistency is minimized, with the centralization of company data. Lastly, the use of a database management system enhances the competitiveness, capability and control of the decision making process; since it allows for the retrieval of information in analyzed form.

Ways in which data warehouses and data mining could be used to support data processing and trend analysis

Data warehouses are increasingly becoming part of modern-day business technologies. They can be used in various ways to advance data processing and trend analysis. To begin with data processing, they are used to integrate data that is stored in various databases, hence making it easier to retrieve, analyze and report. Secondly, data warehouses assist in the analysis of trends, since it allows business executives to work on multiple stores of both operational data to advance quick response to market shifts and to make better business decisions. Lastly, data warehouses stores vast data, hence allowing business to enhance their knowledge of markets and consumers (Joseph, 2013).


Joseph, M. V. (2013). Significance of data warehousing and data mining in business applications. International Journal of Soft Computing and Engineering (IJSCE) ISSN, 2231-2307.

Rob, P., Coronel, C., & Morris, S. (2013). Database systems: Design, implementation, and management. Boston, MA: Course Technology, Cengage Learning/

Velicanu, M., & Matei, G. (2007). Database Vs Data Warehouse. Informatica Economica, 11(3), 91-95.

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