Data Extraction Engine for Multi-Entity Transaction Processing
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Solution Overview
Problem
Current ETL techniques face challenges in efficiently processing and extracting data from multiple sources, particularly in identifying and generating data sets based on specific entity types and transaction data within defined time ranges, while also handling complex relationships between parent and child entity types.
Innovation Solution
A data processing system that receives transaction data from various applications, determines data extraction configurations associated with a requested entity type, processes these configurations to generate data sets including instances of the entity type and related types, and stores the data for further processing or analysis, using a data extraction engine to load, query, and store the data in a structured format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data extraction configurations are processed to generate data sets including multiple entity types, then data completeness and relationship handling are improved, but processing time and system complexity increase
Solution Approach 1:
The system segments the data extraction process by processing each data extraction configuration independently. The data extraction engine retrieves transaction data, generates data sets, and stores them separately in the data storage system. This segmentation allows parallel processing of multiple configurations, improving efficiency while maintaining data completeness across different entity types and their relationships.
2Measurement precision
If data extraction configurations specify multiple data sources with parent and child entity types, then data relationship accuracy is improved, but configuration complexity increases
Solution Approach 1:
The data extraction configuration uses a universal structure that can handle multiple data sources, parent entity types, and child entity types through a single configurable template. The configuration specifies a data source, parent entity type, and child entity type, allowing the same extraction mechanism to universally process different entity relationships without requiring separate complex configurations for each case.
3Adaptability or versatility
If the system extracts and stores data for multiple entity types, then data utility for analysis is improved, but storage requirements and processing overhead increase
Solution Approach 1:
The system performs preliminary data extraction and transformation by processing data extraction configurations before the actual data analysis needs occur. Transaction data is retrieved, transformed into structured data sets with proper entity relationships, and stored in advance in the data storage system. This preliminary action ensures data is ready for various analytical purposes without requiring real-time processing overhead when data is needed.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program may receive a request to extract data for a first entity type. The program may, in response to the request, determine a subset of a plurality of data extraction configurations associated with the first entity type. The program may, for each data extraction configuration in the subset of the plurality of data extraction configurations, process the data extraction configuration based on a set of transaction data to generate a set of data, the set of data comprising a first set of data included in a set of instances of the first entity type and a second set of data included in a set of instances of a second entity type.