API Governance Architecture for Cross-Format Semantic Validation
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Solution Overview
Problem
Existing API development and validation processes lack a comprehensive architecture to ensure consistent and reliable validation of APIs across different formats and formats, leading to inconsistencies and potential reliability issues in user interactions.
Innovation Solution
A governance enforcement architecture is introduced, utilizing a validation framework that supports semantic validation of APIs regardless of format, with a validation engine that applies rulesets through a console and server system, generating reports and notifications based on severity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive validation framework is implemented to ensure consistent API validation across different formats, then validation reliability and data quality are improved, but system complexity and implementation overhead increase
Solution Approach 1:
The validation framework is segmented into distinct functional components: a validation engine that applies rules, a ruleset management system that stores validation criteria, and a notification system that communicates results. This modular segmentation allows each component to be developed and maintained independently, reducing overall system complexity while maintaining comprehensive validation capabilities across multiple API formats.
Solution Approach 2:
The validation framework is designed as a universal system capable of validating multiple API formats (REST, GraphQL, SOAP, etc.) through a single unified engine. The ruleset management system stores format-agnostic validation rules that can be applied across different API types, eliminating the need for separate validation systems for each format and reducing implementation overhead.
2Measurement precision
If semantic validation is applied to ensure high data quality and adherence to rules, then validation precision and data governance are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary validation by applying rulesets to API requests before they are fully processed by the target service. The validation engine pre-checks critical parameters and structure compliance, identifying and rejecting obviously invalid requests early in the processing pipeline. This preliminary action prevents unnecessary computational overhead on clearly invalid requests while maintaining high validation precision for borderline cases.
Solution Approach 2:
The notification system provides immediate feedback to clients about validation results, including specific information about which rules were violated and how to correct them. This feedback mechanism enables clients to quickly understand and fix validation failures, reducing the overall time spent on iterative validation attempts and improving the efficiency of the validation process.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for providing an architecture to support a semantic validation technique. The system includes a governance console that carries out data management functionalities to support the validation. Such functionalities include generating, storing and publishing validation profiles that are used by a validation service for validating an asset, a validation reporter that receives and stores validation reports and performs notification functions to notify relevant individuals of the validation results, as well as a profile runner and associations manager that directly support the validation service.