Final answer:
Normalizing data is used to reduce redundancy and improve data integrity in databases. Update anomalies like insertion, deletion, and modification can occur with redundant data, leading to various data inconsistencies. Functional dependency defines relationships between attributes to help structure databases efficiently.
Step-by-step explanation:
Purpose of Normalizing Data
Normalizing data in the context of databases is essential to reduce redundancy and improve data integrity. The objective is to structure a database according to rules that help support the constraints of data consistency, which includes the elimination of duplicate data. This process relies on constructing tables and defining relationships in such a way that data is divided among the tables as narrowly as possible to minimize the replication of information.
Types of Update Anomalies
Redundant data can lead to several types of update anomalies, including insertion, deletion, and modification anomalies. An insertion anomaly occurs when certain attributes cannot be inserted into the database without the presence of other attributes. A deletion anomaly happens when the deletion of data results in unintended loss of additional data. A modification anomaly refers to the extra effort needed to update data in multiple places due to redundancy.
Functional Dependency Concept
Functional dependency is a fundamental concept within the normalization process in databases. It denotes a relationship between two sets of attributes in which one set, the determinant, uniquely determines the other set. This principle is used to form tables in such a way that each non-key attribute is functionally dependent on the primary key, which helps in structuring databases that accurately reflect the real-world entities and relationships they represent.