What is schema validation?
Schema validation is the process of verifying that data conforms to a predefined structure (schema) that defines expected fields, data types, and constraints.
Understanding schema validation
A schema defines the expected structure of your data: what fields exist, what types they should be, which are required, and what constraints apply. Schema validation checks incoming data against this definition.
Schema components typically include: - Field definitions: Name, data type, description - Required fields: Which fields must have values - Type constraints: String, number, date, boolean, enum - Format constraints: Email, URL, phone number patterns - Value constraints: Min/max, allowed values, regex patterns - Relationships: Cross-field dependencies
Schema validation catches structural problems early, before bad data enters your system and causes downstream issues.
Key points
Verifies data matches a predefined structure
Defines fields, types, and constraints
Catches structural problems early
Can be strict (reject invalid) or lenient (warn)
Schemas should be versioned and documented
Frequently asked questions
What is the difference between schema validation and data validation?
Schema validation checks structure (are the right fields present with the right types?). Data validation checks values (is this email actually valid? is this number in range?). Both are important for data quality.
How do I define a schema for CSV imports?
Define each expected column with: name, data type, required/optional, format constraints, and any validation rules. Tools like Ivandt let you define schemas in JSON or through a visual builder.
Should I use strict or lenient schema validation?
It depends on your use case. Strict validation (reject any invalid data) is good for critical systems. Lenient validation (warn but allow) is better for user-facing imports where you want to help users fix issues.