The study aimed to propose methods to improve the data integrity of the Relational databases such as MS SQL, MySQL and PostgreSQL via record duplication detection. The FODORS and ZAGAT Restaurant database benchmark datasets have been utilized to facilitate the processes involved in preparing and delivering high-quality data. Furthermore, the Levenshtein distance algorithm was used to propose three (3) methods namely: default, eliminating equal string, and knowledge-based libraries to cut duplicate records in the database. In the 70% selected threshold, the average detected duplicate records of 88 out of 112 records in the restaurant dataset. Finally, to efficiently detect duplicate records in the database, depend on the data being analyzed and threshold selected.