Branching Data Monitoring Watchpoints for Pipeline Change Testing
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Solution Overview
Problem
Data pipelines face challenges in managing changes to input data, such as schema alterations and data completeness issues, leading to malfunctions and difficulty in predicting the impact of code changes, with existing monitoring systems failing to effectively model and track the success of code updates.
Innovation Solution
A data monitoring system that branches data models by maintaining a series of models, adding reference pointers, and generating branch models to compare with existing models, providing test results to assess the effects of code changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data pipeline monitoring systems use user defined rulesets to detect malformed inputs and outputs, then monitoring capability is provided, but the systems fail to effectively model code changes and track success of code updates
Solution Approach 1:
The patent segments the data pipeline into multiple executable components or stages, allowing independent monitoring and testing of each component. This segmentation enables the system to track code changes at granular levels while maintaining overall monitoring capability through modular watchpoint insertion points.
Solution Approach 2:
The patent implements preliminary action by performing automated testing and validation before code changes are deployed to production. The system creates test datasets, executes code changes in a controlled environment, and validates results beforehand, enabling reliable tracking of code change success without disrupting production monitoring.
2Ease of manufacture
If data pipeline operators introduce code fixes to correct technical issues, then data pipeline functionality is improved, but it becomes difficult to predict how code changes will affect the pipeline and whether they will successfully resolve issues
Solution Approach 1:
The system performs preliminary validation by executing code changes against test datasets before production deployment. Automated tests verify that code fixes resolve intended issues without introducing new problems, providing precise measurement of code change impact and confidence in successful resolution.
Solution Approach 2:
The patent implements feedback mechanisms that automatically monitor and report the effects of code changes. The system compares pipeline behavior before and after code fixes, providing measurable feedback on whether issues are resolved and whether new issues are introduced, enabling data-driven decisions about code change success.
3Productivity
If data pipelines process large data sets with varying formats, then data processing capability is enhanced, but issues arise including schema creep, completeness problems, and accuracy issues
Solution Approach 1:
The system performs preliminary data validation by creating and executing tests on representative datasets before full pipeline execution. Schema validation, completeness checks, and accuracy verification are performed in advance on sample data, preventing quality issues from propagating through large-scale processing while maintaining high productivity.
Solution Approach 2:
The patent implements self-service monitoring where the data pipeline automatically validates its own output quality through embedded watchpoints and automated tests. The system self-detects schema deviations, completeness issues, and accuracy problems without external intervention, maintaining reliable data quality during high-volume processing through autonomous validation mechanisms.
Data Source
AI summary
Various embodiments comprise systems and methods for operating a data monitoring system to branch data models. In some examples, a data monitoring system maintains a series of models for a data stream. The data monitoring system adds a reference pointer to a position in the series of models. The data monitoring system generates a set of branch models for the data stream and appends the set of branch models to the series of models at the reference pointer. The data monitoring system compares ones of the set of branch models with corresponding ones of the series of models and generates test results based on the comparison. The data monitoring system reports the test results.