Data integrity in relational databases is maintained through a combination of constraints, transactions, and normalization rules. These mechanisms ensure that data remains accurate, consistent, and reliable throughout its lifecycle. By enforcing predefined rules at the database level, relational systems prevent invalid or conflicting data from being stored, even when multiple users interact with the database simultaneously.
First, database constraints play a central role. Primary keys enforce uniqueness for each record (e.g., ensuring no two users share the same ID). Foreign keys maintain referential integrity by linking related tables and preventing actions that would orphan records (e.g., deleting an order while its associated customer record exists). Check constraints validate data against specific conditions (e.g., ensuring an “age” column only contains positive integers). Unique constraints prevent duplicate values in non-primary columns (e.g., ensuring email addresses are unique per user). These rules are applied during data insertion or modification, rejecting operations that violate them. For example, attempting to insert a negative price into a product table with a check constraint would fail immediately.
Second, transactions and ACID properties (Atomicity, Consistency, Isolation, Durability) ensure data consistency during complex operations. A transaction groups multiple operations into a single logical unit—either all succeed or all fail. For instance, transferring funds between bank accounts requires deducting from one account and crediting another; transactions ensure both steps complete or neither does. Databases use locking mechanisms or multiversion concurrency control (MVCC) to isolate transactions, preventing conflicts like two users overwriting the same data. For example, PostgreSQL’s MVCC allows readers to view consistent snapshots of data without blocking writers, maintaining integrity under concurrent access.
Finally, normalization reduces redundancy and dependency issues by structuring data into logical tables. For example, splitting customer addresses into a separate table instead of duplicating them in every order record minimizes update anomalies. Additionally, triggers and stored procedures can enforce custom business rules (e.g., automatically validating a discount percentage before applying it). While application-layer validation is useful, database-level safeguards remain critical because they act as a last line of defense against invalid data, independent of how the data is inserted—whether via an application, direct SQL query, or migration script.