API-Based Data Lineage Tracking with Conditional Logging
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Solution Overview
Problem
Existing data lineage solutions are not fine-grained, require manual documentation, and log every transformation, leading to inaccuracies and overwhelming logs, making it difficult to determine the cause of data transformation issues across multiple environments.
Innovation Solution
A system and method using an API that tracks data lineage by logging only when specific conditions are met, allowing for rule-based, trigger-initiated logging, and recording data identifiers or values, with the ability to write directly to a Kafka or asynchronous messaging platform for centralized logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing solutions log every transformation, then complete data lineage is captured, but the volume of logs becomes overwhelming and difficult to review
Solution Approach 1:
The patent extracts only the essential and meaningful data lineage events from the complete transformation log. By using intelligent sampling and filtering mechanisms, the system identifies and logs only significant transformations that affect data quality, accuracy, or business logic, thereby reducing log volume while maintaining reliability.
Solution Approach 2:
The patent applies different logging strategies to different parts of the data transformation pipeline. Critical transformations with high impact on data quality are logged in detail, while less critical transformations use simplified logging or sampling. This local differentiation optimizes the balance between completeness and manageability.
2Loss of information
If manual documentation is required for data lineage, then detailed transformation logic can be captured, but the process becomes time-consuming and prone to outdated information
Solution Approach 1:
The patent implements self-service data lineage capture by automatically instrumenting data transformation pipelines to collect metadata and transformation logic. The system self-documented the lineage information by reading code, configuration files, and transformation definitions directly from the production environment, eliminating manual documentation efforts while keeping information current.
Solution Approach 2:
The patent performs preliminary automated discovery and documentation of data lineage before production changes occur. By establishing baseline lineage information in advance and continuously monitoring for changes, the system maintains accurate documentation without requiring manual updates after each transformation change.
3Measurement precision
If existing solutions are specific to one technical implementation, then detailed tracking is possible, but the solution cannot be applied across multiple environments
Solution Approach 1:
The patent creates a universal data lineage tracking framework that can operate across multiple technical implementations and environments. The system uses environment-agnostic metadata schemas and configurable instrumentation that adapts to different data transformation technologies, making it universally applicable while maintaining precise tracking capabilities.
Solution Approach 2:
The patent employs configurable parameters and metadata schemas that can be adjusted to match different environment requirements. By allowing dynamic configuration of tracking granularity, data formats, and transformation rules, the system maintains measurement precision across diverse environments without requiring environment-specific implementations.
4Measurement precision
If fine-grained control is implemented for data transformation tracking, then accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the data lineage tracking system into modular components: metadata collection agents, event filtering engines, lineage graph builders, and analysis modules. Each component handles a specific aspect of tracking with fine-grained control, reducing overall system complexity while maintaining high accuracy through specialized functionality.
Data Source
AI summary
A system for tracing data lineage includes a non-transitory computer readable medium and a processor. The processor is configured to execute an application programming interface (API). The processor executes a first instance of the API to document a first data lineage in a first data transformation process. The processor executes a second instance of the API to document a second data lineage in a second data transformation process. The processor sends the first data lineage and the second data lineage for storage in the non-transitory computer readable medium.