Data Lineage Graph Simplification for Redundant Pipeline Removal
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Solution Overview
Problem
Complex data pipelines face performance, efficiency, and quality issues due to multiple stages and operations, often leading to inefficient data management and tangled data lineage.
Innovation Solution
A data lineage optimizer apparatus that utilizes data lineage graphs and query plan evaluation to simplify and optimize data pipelines by removing redundant operations, identifying optimal data paths, and improving transformation quality through iterative simplification and alteration processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data pipelines include multiple stages and operations to process large volumes of data from various sources, then data processing capability and versatility are improved, but pipeline complexity and operational difficulty increase
Solution Approach 1:
The patent segments the complex data pipeline into multiple independent optimization components: data lineage graph construction, redundant operation identification module, quality metric evaluation system, and automated optimization engine. Each component handles specific aspects of pipeline complexity independently, allowing the system to process versatile data types without proportionally increasing overall system complexity.
Solution Approach 2:
The patent introduces data lineage graphs as an intermediary data structure that mediates between raw pipeline operations and optimization decisions. The lineage graph captures transformation relationships without requiring direct analysis of complex pipeline dependencies, simplifying the optimization process while maintaining full processing capability.
2Adaptability or versatility
If data pipelines include multiple transformation stages to handle various data formats, then data format versatility is improved, but transformation quality and reliability deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where quality metrics are continuously evaluated at each transformation stage and fed back to the optimization engine. The system uses quality metrics (completeness, validity, timeliness, consistency) to identify problematic transformations and automatically generates optimizations that improve reliability while preserving format versatility.
Solution Approach 2:
The patent performs preliminary construction of data lineage graphs and quality metric evaluations before executing the actual data transformations. This allows the system to pre-identify potential quality issues and prepare optimization strategies in advance, ensuring reliable transformations across multiple data formats without compromising versatility.
3Productivity
If data pipelines perform extensive data transformation and processing operations, then data processing throughput is improved, but operational efficiency deteriorates due to redundant steps
Solution Approach 1:
The patent extracts and identifies redundant operations from the data pipeline by analyzing the data lineage graph. The system specifically targets duplicate transformations, unnecessary data movements, and redundant quality checks, removing these elements while preserving the core processing throughput. This extraction of redundancy improves operational efficiency without sacrificing processing capability.
Solution Approach 2:
The patent changes operational parameters by optimizing transformation configurations based on quality metrics and lineage analysis. The system adjusts processing parameters, data movement frequencies, and transformation intensities to eliminate redundant operations while maintaining throughput, thereby improving ease of operation without reducing productivity.
4Measurement precision
If data pipelines track detailed lineage information across multiple stages, then data traceability and quality monitoring are improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent creates a simplified copy of the pipeline structure in the form of a data lineage graph. This graph copy captures essential transformation relationships and data flow patterns without requiring the full complexity of the original pipeline. The lineage graph enables precise traceability and quality monitoring while maintaining lower system complexity through this abstracted representation.
Data Source
AI summary
According to examples, data lineage optimization of a dataset involves simplifying and optimizing the data lineage by executing a simplification process and an alteration process. The simplification and alteration processes can be executed iteratively on the datasets of a data lake either serially or parallelly. The simplification process optimizes the data pipeline by identifying and removing redundant data operations and simplifies data lineage graphs of datasets in the data lake. The alteration process improves the quality metrics of the datasets by identifying alternate transformations for generating the datasets such that the alternate transformations have higher quality metrics than the original transformations that created the datasets.