Data Validation Library

A Data Validation Library is a data-processing library that supports data validation tasks (through validation rules and data checking mechanisms).

• Context:
  • It can (typically) perform Data Validation Tasks through validation functions:
    • Input: raw data, validation rules, validation constraints
    • Optional Input: custom validators, validation configurations
    • Output: validation results, error messages, validated data
    • Performance Measure: validation speed, error detection rate
  • ...
  • It can (often) enforce Data Type Constraints through type checking.
  • It can (often) validate Data Format Rules through format validation.
  • It can (often) check Data Range Rules through range validation.
  • It can (often) support Custom Validation Rules through rule definition.
  • It can (often) maintain Data Consistency through validation checks.
  • ...
  • It can range from being a Simple Data Validator to being a Complex Validation Engine, depending on its validation capability.
  • It can range from being a Single Format Validator to being a Multi-Format Validator, depending on its format support.
  • It can range from being a Synchronous Validator to being an Asynchronous Validator, depending on its processing model.
  • It can range from being a Standalone Validator to being an Integrated Validator, depending on its integration level.
  • ...
  • It can integrate with Web Frameworks for form validation.
  • It can support Database Systems for data integrity.
  • It can connect to API Gateways for request validation.
  • It can work with ETL Tools for data pipeline validation.
  • ...
• Example(s):
  • Web Validation Librarys, such as:
    • Form Validation Librarys, such as:
      • HTML Form Validator for user input validation.
      • AJAX Form Validator for asynchronous validation.
    • API Validation Librarys, such as:
      • REST API Validator for API request validation.
      • GraphQL Validator for query validation.
  • Data Format Validators, such as:
    • Schema Validation Librarys, such as:
      • JSON Schema Validator for JSON validation.
      • XML Schema Validator for XML validation.
    • Data Type Validators, such as:
      • Type Checking Library for type validation.
      • Format Validation Library for format checking.
  • Business Rule Validators, such as:
    • Domain Rule Librarys, such as:
      • Business Logic Validator for business rule validation.
      • Compliance Validator for regulatory compliance.
  • ...
• Counter-Example(s):
  • Data Parsers, which focus on data parsing without validation.
  • Data Transformers, which perform data transformation without validation checks.
  • Data Serializers, which handle data serialization without validation rules.
• See: Pydantic, Data Quality Framework, Schema Validation System, Input Validation Framework, Validation Rule Engine.

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References

2023

• GBard
  • A data validation library is a software library that provides functions for validating data. Data validation is the process of checking that data meets certain criteria, such as being of a certain type, within a certain range, or having a certain format.
  • Data validation libraries can be used in a variety of applications, such as:
    • Web development: To validate user input before submitting it to a server.
    • Data science and machine learning: To validate data before processing it or using it to train a model.
    • Data integration: To validate data before loading it into a data warehouse or other data store.
    • Data governance: To validate data to ensure that it meets compliance requirements.

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