Data Validation Techniques

Introduction

Data validation ensures data entered into a database is accurate, complete, and reliable. It helps to prevent incorrect, inconsistent, or duplicated data.

Data validation is essential for maintaining data integrity in SQL databases.

Importance

• Reduce data entry and processing errors.
• Ensure consistency across datasets and systems.
• Enhances accuracy of reports and analytics.
• Enforces business automatically.

Types

• Format Validation: Check pattern.
• Range Validation: Ensure values within limits.
• Presence Validation: Requires mandatory fields.
• Referential Validation: Maintains valid relationships between tables.

Key Constraint

UNIQUE

CREATE TABLE tb (
  id INT UNIQUE,
  name VARCHAR(50) NOT NULL
);

DEFAULT

CREATE TABLE tb (
  id INT NOT NULL,
  name VARCHAR(50) NOT NULL
);

CHECK

CREATE TABLE tb (
  id INT PRIMARY KEY,
  name VARCHAR(50) NOT NULL,
  age INT CHECK (age >= 18)
);

Triggers

Example:

CREATE TRIGGER trigger_name
AFTER INSERT ON tb1
FOR EACH ROW
UPDATE tb
SET col1 = col1 + NEW.col2;

Summary

• Constraints, triggers, and procedures can be combined for robust validation.
• Always validate at the database level.
• Use staging tables for import validation.
• Regularly audit and document validation rules.
• Good validation = clean, trustworthy data.