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This article explains how to encapsulate JSON data format validation in Pytest by creating a reusable JSONValidator utility, defining JSON Schemas, writing test cases that check structure, field types, and values, and executing them to ensure efficient and maintainable API testing.
The article provides ten code examples demonstrating how to validate JSON data with Python's jsonschema library, covering basic validation, required fields, nested schemas, arrays, uniqueness, enums, ranges, pattern matching, conditional logic, and custom error messages.
Database testing ensures data integrity, security, and quality by validating data mapping, ACID properties, schema, triggers, stored procedures, and field constraints through both manual and automated methods, using SQL queries, CRUD operations, and specialized tools to verify that applications interact correctly with underlying databases.