Multi-Point Reference Data Model for Data Pipeline Migration Validation
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Solution Overview
Problem
Complex data pipeline migrations face challenges in maintaining consistent operation and error tracing due to complex logic and large volumes of data, making it difficult to validate operations and identify error origins during platform migrations.
Innovation Solution
A multi-point reference data model and placement model are implemented to support both forward and reverse referencing, enabling flexible and reliable operation, faster error tracing, and efficient validation through a pipelined multiple-tier test stack with data and job validation logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex data pipeline migrations are performed with complex logic and large volumes of data, then data processing capability is improved, but error tracing difficulty increases and operation consistency becomes harder to maintain
Solution Approach 1:
The patent implements a multi-point reference data model that segments the data pipeline into multiple validation points (extraction tier, script generation tier, test tier, validation tier). Each tier independently validates specific aspects of data migration, breaking down the complex error tracing problem into manageable segments that can be individually monitored and validated.
Solution Approach 2:
The patent introduces a multi-point reference data model as an intermediary layer between data sources and data destinations. This model includes reference data sets and validation logic that mediate the migration process, enabling automated validation and error detection without requiring direct complex comparisons between source and target systems.
2Reliability
If validation of numerous computational components and data is performed to ensure consistent operation, then operation consistency is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal multi-point reference data model that can validate multiple types of computational components (extraction processes, transformation logic, loading operations) using a unified framework. The same reference data model and validation tier serve multiple validation purposes, reducing overall system complexity while maintaining comprehensive validation coverage.
Solution Approach 2:
The validation system performs self-validation through automated comparison of reference data with actual migrated data. The multi-point reference data model contains expected outcomes and validation rules that automatically check migration consistency without requiring external manual validation, reducing the need for complex external verification systems.
3Loss of time
If multi-point reference data model with forward and reverse referencing is implemented, then error tracing speed is improved, but data model complexity increases
Solution Approach 1:
The patent pre-establishes forward and reverse reference relationships in the multi-point reference data model before migration execution. Reference data sets are pre-configured with validation rules and relationships, enabling immediate error tracing and back-tracing during migration without requiring complex real-time analysis, thus reducing error tracing time despite the model's structural complexity.
Data Source
AI summary
A system may execute a pipelined multiple-tier test stack to support migration of computing resources via a migratory data stream. Via the pipelined multiple-tier test stack, the system may perform extract, transform, and load operations on the migratory data stream. The extract, transform, and load operations may be used to identify applications that may undergo testing. At a generation tier of the pipelined multiple-tier test stack, the system may generate test scripts, which may be used to test the application. The tests may be validated by the system via a validation tier of the pipelined multiple-tier test stack. To govern the operations, the pipelined multiple-tier test stack may rely on a multi-point reference data model.