Data Virtualization Adapter for API Integration
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Solution Overview
Problem
Integration platforms face significant complexity in implementing comprehensive data-driven analysis across diverse resources and tools due to the need for custom code to transform unstructured data from APIs and other sources, leading to extensive legacy code management burdens.
Innovation Solution
A data bridge adapter within the integration platform creates an entity-relationship model from any API or data source, allowing users to map and transform data without custom code, supporting versioning, schema change resolution, and compatibility detection, and enabling data visualization tools to analyze the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom code is used to transform unstructured data from APIs and data sources, then data integration capability is achieved, but device complexity and legacy code management burden increase significantly
Solution Approach 1:
The patent introduces an intermediary component (data virtualization layer with entity-relationship model) between the diverse data sources and the integration platform. This intermediary automatically transforms unstructured API data into structured relational representations, eliminating the need for custom transformation code for each data source while maintaining high adaptability to different APIs and data formats
Solution Approach 2:
The patent creates virtual copies of data sources through entity-relationship models that replicate the structure and semantics of various API data formats. These virtual models allow the system to work with diverse data sources without directly implementing custom integration code for each source, reducing complexity while preserving data integration capabilities
2Productivity
If comprehensive data-driven analysis is implemented across diverse resources, then analytical capability is improved, but the need for custom transformation code increases complexity
Solution Approach 1:
The patent transforms data from various API formats into a standardized entity-relationship model structure, changing the parameter representation of data from heterogeneous formats to a unified relational schema. This parameter transformation enables comprehensive data analysis across diverse resources without requiring custom transformation code for each data source
3Ease of operation
If data virtualization is implemented to eliminate custom code, then ease of operation is improved, but compatibility detection and schema change resolution capabilities must be enhanced
Solution Approach 1:
The patent implements feedback mechanisms that automatically detect schema changes in data sources and adjust the entity-relationship models accordingly. The system monitors changes in API data formats and provides feedback to update the virtualization layer, maintaining compatibility without requiring manual intervention while keeping the system easy to operate
Data Source
AI summary
Disclosed herein are system, method, and device embodiments for a data bridge adapter in a data integration platform that models any application programming interface as an entity-relationship model. This technique allows an individual using an integration platform to map and transform the entity-relationship model without having to create any custom code. The user may specify target parameters as part of an ETL process, and the entity relationship model may allow appropriate data and API calls to be generated to pass the data to the specified target. By creating an entity-relationship model from any API, the technique further supports versioning, schema change resolution, compatibility detection and other features. Furthermore, data visualization software may use the entity-relationship model to allow users to explore and analyze the data represented in the entity-relationship model.