Self-Service Dataset Validation Before Processing and Publication
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Solution Overview
Problem
Existing data processing systems lack real-time validation of incoming and outgoing datasets against user-defined data quality standards, leading to the consumption and publication of poor-quality data.
Innovation Solution
A data processing system that allows users to define user-defined data quality rules for incoming and outgoing datasets, performing real-time validation checks to ensure compliance with these rules, aborting or discarding datasets that fail, and alerting users to take corrective action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data quality validation checks are performed on incoming and outgoing datasets, then data quality is improved, but system complexity increases
Solution Approach 1:
The data quality control system is segmented into distinct modular components: incoming dataset validation module, data processing job execution module, outgoing dataset generation module, and outgoing dataset validation module. Each module performs a specific validation or processing function, allowing the system to maintain high data quality through comprehensive checks while managing complexity through clear separation of concerns and independent, reusable validation components.
2Reliability
If user-defined data quality rules are implemented for incoming and outgoing datasets, then data quality control is improved, but ease of operation deteriorates
Solution Approach 1:
The system enables users to define and configure their own data quality rules through an intuitive interface. Users can specify validation criteria for incoming datasets (such as required fields, data types, and format constraints) and outgoing datasets (such as completeness thresholds and quality metrics). This self-service capability allows users to tailor data quality control to their specific needs without requiring complex system configuration or programmer intervention, thereby maintaining ease of operation while achieving precise data quality control.
3Loss of energy
If data processing jobs are aborted for failing incoming datasets, then resource waste is reduced, but productivity decreases
Solution Approach 1:
The system performs preliminary validation of incoming datasets against user-defined quality rules before initiating data processing jobs. By checking data quality metrics, completeness, and format compliance in advance, the system identifies and rejects不合格 datasets before they consume processing resources. This preliminary action prevents waste of computational resources on datasets that would fail processing anyway, while maintaining productivity by allowing valid datasets to proceed through the processing pipeline without delay.
4Reliability
If outgoing datasets are validated before publication, then data quality is improved, but loss of time increases
Solution Approach 1:
The outgoing dataset validation process operates continuously and asynchronously with the data processing pipeline. Validation checks are performed in real-time as datasets are generated, and the system maintains continuous monitoring of data quality metrics. This continuous validation approach ensures that only high-quality datasets are published to downstream systems while minimizing delays, as the validation occurs in parallel with processing operations rather than as a sequential bottleneck.
Data Source
AI summary
In some implementations, a system may obtain a first set of data quality rules for an incoming dataset and a second set of data quality rules for an outgoing dataset. The system may perform a first data quality validation check for the incoming dataset based on a comparison of data quality metrics associated with the incoming dataset and the first set of data quality rules, and may process the incoming dataset to generate an outgoing dataset based on the incoming dataset passing the first data quality validation check. The system may perform a second data quality validation check for an outgoing dataset based on a comparison of data quality metrics associated with the outgoing dataset and the second set of data quality rules, and may publish the outgoing dataset to a downstream data sink based on the outgoing dataset passing the second data quality validation check.