Long Questions
Short Questions
MCQ’s
This chapter discusses the concepts of data integrity and normalization in databases, which are essential for maintaining the accuracy and efficiency of data management.
Data Integrity
Data integrity refers to the accuracy and consistency of data within a database. It ensures that the data is reliable and valid. There are several types of data integrity:
- Entity Integrity:
- Ensures that each table has a primary key that uniquely identifies each record.
- Primary keys cannot contain null values.
- Referential Integrity:
- Maintains consistency between tables by ensuring that foreign keys correctly reference primary keys in other tables.
- Prevents orphan records (records that reference a non-existent entry).
- Domain Integrity:
- Ensures that all entries in a column adhere to a defined data type and value range.
- Helps maintain valid data formats and values.
- User-Defined Integrity:
- Involves specific rules defined by users or business requirements to ensure data adheres to certain conditions.
Normalization
Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. The normalization process involves several normal forms:
- First Normal Form (1NF):
- Requires that all entries in a column are atomic and that there are no repeating groups of data.
- Second Normal Form (2NF):
- Builds on 1NF by ensuring that all non-key attributes are fully functionally dependent on the primary key, eliminating partial dependencies.
- Third Normal Form (3NF):
- Ensures that all non-key attributes are not only dependent on the primary key but also independent of each other, eliminating transitive dependencies.
- Boyce-Codd Normal Form (BCNF):
- A stronger version of 3NF that addresses certain anomalies that 3NF might not cover, requiring that every determinant is a candidate key.
Importance of Normalization
- Reduces data redundancy, leading to efficient data storage.
- Improves data integrity by ensuring consistent data entries.
- Enhances query performance by simplifying the database structure.
Conclusion
Understanding data integrity and normalization is crucial for effective database management, ensuring that data remains accurate, consistent, and efficiently organized.