Heterogeneous Data Ingestion With User Resolution Without ETL
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Solution Overview
Problem
Existing credit data systems face challenges in efficiently handling large volumes of heterogeneous credit events due to rigid database schemas, resource-intensive ETL processes, and slow data analysis, leading to inaccuracies and delays in generating credit reports and statements.
Innovation Solution
A credit data system that performs batch indexing and lazy data interpretation, using inverted personal identifiers and domain categorization to efficiently cluster and annotate data without ETL processes, allowing for real-time credit data access and reduced defect formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional ETL processes and rigid database schemas are used to handle credit data, then data organization and processing can be achieved, but system complexity increases and processing speed decreases
Solution Approach 1:
The patent extracts and eliminates the ETL (Extract-Transform-Load) process from the data pipeline. By using an event-driven architecture where data is ingested directly as events without transformation steps, the system removes the complexity and time-consuming nature of traditional ETL processes while maintaining data organization through event schemas and categorization.
Solution Approach 2:
Instead of the traditional approach of transforming data to fit rigid database schemas, the patent inverts the approach by using flexible event schemas that can accommodate various data formats natively. The system organizes data around events rather than forcing events into predefined table structures, thereby reducing system complexity and improving processing speed.
2Reliability
If comprehensive data validation and matching rules are applied to ensure data accuracy, then data quality improves, but processing time increases
Solution Approach 1:
The patent applies data validation rules and matching criteria as event filters during the initial event ingestion phase rather than performing comprehensive validation later. This preliminary filtering approach validates data at the point of entry, ensuring data accuracy while minimizing processing time by avoiding redundant validation steps in subsequent stages.
Solution Approach 2:
The event-driven architecture enables data to be validated and categorized automatically through predefined event schemas and matching rules that operate autonomously. The system self-validates incoming events against schema definitions and automatically routes them to appropriate processing queues, reducing the need for manual intervention and minimizing processing time while maintaining high data accuracy.
Data Source
AI summary
This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.