Automated Data Comparison Engine for Heterogeneous Systems
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Solution Overview
Problem
Existing data comparison tools are limited in their ability to efficiently compare and transition data between heterogeneous data sources, requiring manual intervention and being constrained by schema limitations, database interface capabilities, and the need for identical data types in source and target columns, leading to time-consuming and error-prone processes.
Innovation Solution
A method and system for data comparison and transition that involves connecting computing devices to source and target systems, using comparison information to map and extract data, and generating a score/report based on the comparison, allowing for data transformation and validation between heterogeneous data sources, including the use of a graphical user interface for user input and automated processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data testing is performed, then data validity can be checked, but the process is time consuming and error prone
Solution Approach 1:
The system performs automated data comparison and validation without requiring manual intervention. The comparison engine automatically connects to source and target systems, extracts data, compares it according to defined criteria, and generates validation reports, enabling the system to self-validate data integrity
Solution Approach 2:
Manual testing processes are replaced with an automated software-based comparison engine that uses computational algorithms to perform data extraction, comparison, and validation, substituting human mechanical operations with automated electronic processing
2Adaptability or versatility
If existing comparison software is used, then data comparison can be performed, but it is limited to files of the same type and requires identical data types
Solution Approach 1:
The system dynamically adjusts comparison parameters and data type mappings based on the source and target data structures. It allows flexible configuration of data type conversions and comparisons between heterogeneous data types by modifying comparison criteria according to the specific data being compared
Solution Approach 2:
The comparison engine is designed to handle multiple data types and formats universally. It can compare data across different file types, database systems, and data structures through a unified comparison framework that adapts to various data formats without requiring identical source and target schemas
3Extent of automation
If sophisticated automated tools are used, then data aging and conversion can be performed, but database interface limitations and schema constraints remain
Solution Approach 1:
The system introduces an intermediary comparison engine that acts as a mediator between different database systems and data formats. This intermediary layer handles data extraction, transformation, and comparison across heterogeneous systems, overcoming direct interface limitations and schema constraints by translating between different database protocols and data structures
Data Source
AI summary
Systems and methods are illustrated for transitioning data from one data store to another data store. The transition may occur according to comparison information received through a graphical user interface. For example, the comparison information may map data or columns in a source data store to data or columns in a target data store. A score may also be generated based on a comparison of the source and target. A report may indicate a successful or failed data transition based on the score.