Data Modeling For A Relational Database Management System

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The need to store and evaluate data is a perpetually growing field in the world of information systems. From the days of using flat files to very large database management systems that store petabytes of data in real time, the practice of building information from data continues to evolve. Today, the relational data model is quite ubiquitous and is used in a plethora of information systems ranging from accounting systems, banks, retail business, and scientific usage. It is important to understand the concepts involved in data modeling for a relational database management system in order to build an effective and efficient system.
Data models weren’t as sophisticated in the early days as they are today. In the 1960’s and 70’s the first generation data models were comprised of an ad hoc file system with no concept of relationships between the files (Coronel & Morris, 2015). For instance, one file might contain rows of customer records while another would house invoices. For simple data, file systems worked, but for large sets of interconnecting data, a data processing specialist was needed to create a program that fetched the proper data, analyze it, formatted it, and presented it in a report that made sense to the end user. For every new query, the data processing specialist needed to create a separate application. Files became increasingly cumbersome the more that were added and they duplicated quite a bit of data since there were no relationships between files. The time
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