Data Pipeline Controller for Dynamic Schema Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data pipeline systems lack efficient methods for dynamically generating and managing data schemas and ontologies for new data pipeline components, leading to complexities in integrating new data sources and targets, and requiring significant human intervention and expertise.
Innovation Solution
A data pipeline controller that automatically generates data schemas and ontologies for new data pipeline components by mapping them to existing types, allowing for real-time integration and management of data pipelines with minimal human interaction, using a system that includes modules for scheduling, AI/ML, security, and data schema generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to create data schemas and ontologies for new data pipeline components, then accuracy and completeness can be ensured through expert review, but the process requires significant human intervention and expertise, reducing productivity and increasing time consumption
Solution Approach 1:
The system enables self-service by allowing new data pipeline components to automatically generate their own data schemas and ontologies through AI/ML-based mapping to existing component types, eliminating the need for manual expert creation while maintaining accuracy through automated validation
Solution Approach 2:
The system performs preliminary action by pre-defining ontologies and data schemas for existing data pipeline component types in a catalog, which then serve as templates for automatically generating schemas for new components through mapping, significantly reducing the time required for integration
2Reliability
If manual expert intervention is used to integrate new data sources and targets, then integration accuracy can be maintained, but the device complexity and difficulty of operation increase due to the need for specialized knowledge
Solution Approach 1:
The system introduces an intermediary AI/ML-based data pipeline controller that automatically maps new data sources and targets to existing component types using ontologies and data schemas, maintaining integration accuracy while eliminating the need for manual expert intervention and specialized knowledge
Solution Approach 2:
The system achieves universality by creating a catalog of reusable ontologies and data schemas that can be applied across multiple different data pipeline component types, allowing the same automated process to handle diverse integration scenarios without requiring type-specific expert knowledge
3Stability of the object's composition
If traditional methods are used to manage data pipeline components, then system stability is maintained, but adaptability to new data sources and targets is reduced due to the lack of dynamic schema generation capabilities
Solution Approach 1:
The system implements dynamics by enabling automatic generation and updating of data schemas and ontologies for new data pipeline components through AI/ML-based mapping to existing types, allowing the system to adapt to new data sources and targets in real-time while maintaining stability through validated schema templates
Solution Approach 2:
The system uses copying by replicating proven ontologies and data schemas from existing data pipeline component types in the catalog to create schemas for new components, ensuring consistency and stability while enabling rapid adaptation to new component types through template-based generation
Data Source
AI summary
A processing system including at least one processor may obtain a first ontology of a first type of data pipeline component, map the first ontology to a second ontology for a second type of data pipeline component that is stored in a catalog of data pipeline component types, provide a second data schema for the second type of data pipeline component as a template for a first data schema for the first type of data pipeline component, and add the first type of data pipeline component to the catalog of data pipeline component types, where the adding comprises storing the first ontology and the first data schema for the first type of data pipeline component in the catalog of data pipeline component types.