ETL Complexity Management Framework for Data Error Identification
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Solution Overview
Problem
ETL processes face complexity in managing data conversions across different systems, leading to conversion errors due to varying data formats, making it difficult to identify and manage errors effectively.
Innovation Solution
An ETL complexity management framework that includes defining ETL job definitions, data asset definitions, and dependency definitions, allowing for error identification, rollback, and forward processes, along with a system for monitoring and managing dependencies between data assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data conversion is performed across multiple systems with different formats, then data integration capability is improved, but conversion errors increase and become difficult to identify
Solution Approach 1:
The patent introduces an intermediary error identification system that acts as a mediator between the ETL process and the data. This system includes error identification logic that monitors data conversion processes, captures error information, and provides structured error reporting. The intermediary layer enables complex multi-system data integration while maintaining reliability by detecting and reporting conversion errors systematically.
2Difficulty of detecting and measuring
If comprehensive data monitoring and error tracking is implemented, then error identification capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the error management functionality into distinct modular components: error identification logic, error information capture mechanisms, and structured error reporting systems. Each component handles a specific aspect of error management, making the overall system more manageable despite comprehensive monitoring. The segmented architecture allows the system to provide detailed error tracking without overwhelming complexity.
3Reliability
If data assets are rolled back to previous versions, then data integrity is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary version control and snapshot mechanisms that capture data asset states before ETL operations. By pre-establishing version checkpoints and maintaining historical data states, the system enables rapid rollback to previous versions when errors are detected. This preliminary action eliminates the need for time-consuming manual restoration processes.
Data Source
AI summary
Extract, transform, and load application (ETL) complexity management framework systems and methods are described herein. The present disclosure describes systems and methods that reduce the complexity in managing ETL flow and correcting errant data that is subsequently identified. One or more methods include defining an ETL job definition, defining a data asset definition, defining a data asset dependency definition, receiving an ETL flow to provide execution of one or more ETL flow steps, providing retrieval of data from a source data asset, applying a data control to the source asset data, and producing an ETL job registration, a data asset status, a latest asset available date, a data asset consumer identifier, and a target data asset based on at least one of the ETL job definition, the data asset definition, the data dependency definition, and the source asset data.