Data Integrity:

1. Definition: Data integrity refers to the accuracy, consistency, and reliability of data stored in a database over its entire lifecycle.

2. Importance of Data Integrity:

   • Ensures the reliability and trustworthiness of data.

   • Guarantees that data meets certain standards and constraints.

   • Prevents data corruption, loss, or unauthorized modifications.

3. Types of Data Integrity:

   • Entity Integrity: Ensures that each row in a table is uniquely identified by a primary key and that the primary key column does not contain null values.

   • Referential Integrity: Maintains the consistency between related tables by enforcing relationships and preventing orphaned or inconsistent data.

   • Domain Integrity: Ensures that data within a column adheres to specified data types, formats, ranges, or constraints.

   • User-defined Integrity: Additional integrity rules defined by users or application requirements.

4. Techniques for Enforcing Data Integrity:

   • Primary Keys: Establishes a unique identifier for each row in a table.

   • Foreign Keys: Defines relationships between tables and ensures referential integrity.

   • Check Constraints: Defines specific conditions that data must meet to maintain integrity.

   • Data Validation: Verifies the accuracy and integrity of data before it is entered into the database.

   • Triggers: Automatically executes actions or validations based on specified events or conditions.

Constraints:

1. Definition: Constraints are rules or conditions that are applied to the data in a database to enforce integrity, consistency, and validity.

2. Common Types of Constraints:

   • Primary Key Constraint: Ensures that the primary key column(s) contain unique and non-null values.

   • Foreign Key Constraint: Enforces referential integrity by ensuring that values in a column match values in a related table's primary key.

   • Unique Constraint: Ensures that the values in a column or a combination of columns are unique.

   • Check Constraint: Validates that data meets specific conditions or expressions.

   • Not Null Constraint: Requires a column to contain a non-null value.

   • Default Constraint: Specifies a default value for a column when no value is provided during insertion.

3. Purpose of Constraints:

   • Maintain data integrity and consistency.

   • Prevent invalid or inconsistent data from being inserted, updated, or deleted.

   • Enforce business rules and data validation requirements.

   • Improve data quality and reliability.

4. Benefits of Constraints:

   • Promote data integrity and accuracy.

   • Improve data consistency and reliability.

   • Simplify application logic by offloading data validation to the database.

   • Enhance data protection and security.

   • Provide better error handling and error reporting.