Massive-Scale Heterogeneous Data Ingestion for Real-Time User Resolution
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Solution Overview
Problem
Existing credit data systems face challenges in efficiently handling large volumes of heterogeneous credit events due to sheer data volume, varying data formats, and slow analysis, leading to inaccuracies and resource-intensive ETL processes that introduce defects and delays in generating credit reports.
Innovation Solution
A credit data system that performs batch indexing and lazy data interpretation, using hash functions to cluster unique identities, assign inverted personal identifiers, and annotate data without ETL, enabling real-time credit data access and reduced defect formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ETL processes are used to handle large volumes of heterogeneous credit events, then data can be organized and analyzed, but the processing time increases and defects are introduced
Solution Approach 1:
The patent extracts and eliminates the ETL transformation step from the data processing pipeline. By using an event-driven architecture that directly ingests and processes credit events in their native formats without extraction, transformation, or loading operations, the system removes the time-consuming and defect-prone ETL process while maintaining data organization and analysis capabilities through event streaming and real-time processing.
Solution Approach 2:
The system performs preliminary data validation and enrichment at the point of data ingestion rather than during subsequent ETL processes. By validating event data structures, schemas, and formats upfront using schema validation and data enrichment services, the system prevents defects before they propagate through the processing pipeline, reducing both processing time and error rates.
2Stability of the object's composition
If ETL processes are used to standardize heterogeneous data formats, then data consistency is achieved, but system complexity and resource consumption increase
Solution Approach 1:
The patent changes the approach to data standardization from active transformation to passive normalization. Instead of using ETL processes to actively transform heterogeneous data formats into a standardized structure, the system accepts data in various formats and uses parameter-based event schemas to define expected data structures. Data consistency is achieved through schema validation and type checking rather than format transformation, significantly reducing system complexity.
Solution Approach 2:
The system implements a universal event-driven architecture that can handle multiple data formats and sources through a single processing pipeline. By using format-agnostic event schemas and flexible data validation mechanisms, the system achieves data consistency across heterogeneous sources without requiring separate ETL processes for each data type, thereby reducing overall system complexity while maintaining stability.
3Use of energy by moving object
If batch processing is used to analyze credit events, then resource consumption is reduced, but real-time access to credit data is delayed
Solution Approach 1:
The patent implements a hybrid processing model that combines real-time event streaming with periodic batch aggregation. Critical credit events are processed immediately through the event-driven pipeline providing real-time access, while less time-sensitive aggregations and analytics are performed in periodic batches to optimize resource consumption. This periodic action allows the system to balance between real-time performance and resource efficiency for different operational requirements.
Solution Approach 2:
The system segments credit event processing into multiple priority levels and processing streams. High-priority events requiring immediate analysis are routed through real-time processing paths with lower resource constraints, while lower-priority events are batched for efficient bulk processing. This segmentation allows simultaneous real-time and batch processing, providing fast data access for critical operations while maintaining resource efficiency for routine analytics.
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.