Duplication of data is known as
The correct answer is Data redundancy.
Data redundancy refers to the duplication of data within a database or across multiple databases. It occurs when the same data is stored multiple times in different locations or tables. Here is a detailed explanation of data redundancy:
Causes of data redundancy:
- Lack of normalization: When a database is not properly normalized, it can lead to data duplication.
- Poor database design: Inefficient database design can result in redundant data storage.
- Data integration: When data is integrated from multiple sources, there may be instances of duplicate data.
Effects of data redundancy:
- Increased storage requirements: Duplicate data occupies additional storage space.
- Data inconsistency: Redundant data can lead to inconsistencies and discrepancies when updates or changes are made in one location but not in others.
- Decreased data integrity: Redundant data increases the chances of data inconsistencies and errors, affecting the overall integrity of the database.
- Increased maintenance efforts: It becomes more challenging to keep redundant data up-to-date and synchronized.
Ways to handle data redundancy:
- Normalization: Proper normalization of the database can help eliminate or reduce data redundancy.
- Use of primary keys and foreign keys: Establishing relationships between tables using primary keys and foreign keys can prevent duplicate data.
- Data validation: Implementing data validation rules can ensure that only valid and non-redundant data is entered into the database.
- Regular data cleaning: Periodically review and clean up the database to remove duplicate or unnecessary data.
In conclusion, data redundancy refers to the duplication of data and can lead to storage inefficiencies, data inconsistencies, and maintenance challenges. Proper database design, normalization, and data validation techniques can help minimize data redundancy and improve data integrity.