Data integrity in DBMS refers to the overall accuracy, completeness, and consistency of data stored in a database. It is maintained by a collection of processes, rules, and standards implemented during the design phase. Data integrity is essential because it ensures that the data stored in a database remains complete, accurate, and reliable, no matter how long it’s stored or how often it’s accessed.

Here are some key points about data integrity in DBMS:

• Types of data integrity: There are four main types of data integrity: entity integrity, referential integrity, domain integrity, and user-defined integrity.
• Entity integrity: This ensures that each row in a table is uniquely identified so that data can be retrieved separately.
• Referential integrity: This refers to the series of processes that make sure data is stored and used uniformly. It involves creating rules embedded into the databases structure about how data should be stored and used.
• Domain integrity: This ensures that the data entered into a specific column of a table is of the correct data type and within the acceptable range of values.
• User-defined integrity: This refers to any additional rules or constraints that are specific to a particular database or application.

Data integrity is not the same as data security, although the two concepts are related. Data security involves protecting data from both external and internal threats and maintaining the privacy of its subjects. Threats to a dataset’s integrity can include human error, malware/hacker interference, disk crashes, bugs, and physical device damage.

In summary, data integrity in DBMS refers to the accuracy, completeness, and consistency of data stored in a database. It is maintained by a collection of processes, rules, and standards implemented during the design phase. There are four main types of data integrity: entity integrity, referential integrity, domain integrity, and user-defined integrity.