Databases hold critical data that is valuable for both consumers and businesses. As a business expands, the reliance on a healthy and working database becomes imperative for its business operations. Hence, it is essential to maintain and monitor relational databases as they are the backbone for business longevity and growth. This project aims to retrofit the concept of an inventory management system to database systems. The scope and result of this project are to (1) host the system in a webserver, (2) capability to connect to any of the popular relational database systems (SQL Server, Postgres, Oracle, or MySQL), (3) monitor a database’s availability and capacity, and (4) aggregate and display historical data into graphs and tables. The resulting system satisfied the requirements mentioned above. The system is hosted on AWS EC2 Windows 2019. Through the web interface, you can monitor and analyze historical data. With the pyodbc library, it can Extract-Transform-Load data from the target database and then load it to the local database. This method is possible with the help of SQLs native for each target database. Even though numerous database monitoring tools are on the market, this system is a low-cost alternative. Also, the system is still in its infancy. It suffered from project ailments that hindered the progress of this project, such as underestimating tasks, shortage of time, and delayed testing. In essence, the system still satisfies the benchmarks for testing, and it is subjected to future improvements.

{"references": ["A. O. Madamidola, O. A. Daramola, and K. G. Akintola, \"Web-based intelligent inventory management system,\" International Journal of Trend in Scientific Research and Development, vol. 1, no. 4, May 2017 [Online]. Available: https://www.researchgate.net/publication/317276986_WEB_- _BASED_INTELLIGENT_INVENTORY_MANAGEMENT_SYSTEM. [Accessed: 12-Jan 2022]", "D. V. Aken, D. Yang, S. Brillard, A. Fiorino, B. Zhang, C. Bilien, and A. Pavlo, \"An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems,\" Carnegie Mellon Database Research Group, 01-Mar-2021. [Online]. Available: https://db.cs.cmu.edu/papers/2021/p1241-aken.pdf. [Accessed: 12-Jan-2022]."]}

Master's Thesis / Capstone