Answer: Functional dependencies in a database can lead to update anomalies due to the way they structure the relationship between columns of data.
When data isn’t normalized properly, it can lead to redundancy across multiple rows or tables, causing three main types of update anomalies: insertion, deletion, and modification.
- Insertion Anomaly: Occurs when additional, potentially unrelated, information is needed to insert data. For instance, in a student database, a new student cannot be added unless they enroll in a course.
- Deletion Anomaly: This happens when deleting data removes more information than intended. Removing a student’s last course, if course and student info are stored together, might accidentally delete all student details.
- Modification Anomaly: Arises when redundant data requires multiple updates. If a student’s address is duplicated across rows for course enrollments, updating the address necessitates changing it in each record.
These anomalies make data management more complex, raise the chance of errors, and threaten data integrity. Proper normalization, organizing databases to minimize redundancy and dependency, can alleviate these problems by storing each piece of information uniquely.