Distributed Data Pipeline Lineage Using Universal Hash Identifiers
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Solution Overview
Problem
Existing technologies struggle to track and clone distributed data processing pipelines due to inconsistent data artifact naming across multiple disconnected data processing sites, leading to challenges in metadata and data lineage tracking, which is crucial for regulatory compliance and debugging.
Innovation Solution
A computerized system generates unique hash content values for data artifacts across processing stages and creates a data processing pipeline abstraction hierarchy to consistently identify and associate data artifacts, enabling the construction of accurate data lineage representations for distributed data processing pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data processing pipelines are distributed across multiple disconnected data processing sites, then processing capability and scalability are improved, but data artifact tracking and lineage consistency deteriorate due to naming inconsistencies
Solution Approach 1:
The patent introduces a central repository as an intermediary component that collects, stores, and manages metadata from multiple distributed data processing sites. This central repository acts as a mediator that reconciles naming inconsistencies by providing a unified view of data artifacts across all sites, enabling consistent tracking and lineage management without requiring the sites to be directly connected to each other.
Solution Approach 2:
The patent transforms the identification parameter of data artifacts from site-specific names to universal identifiers that are consistent across all distributed sites. By changing the parameter used for identification (from local naming conventions to standardized universal identifiers), the system maintains tracking consistency while allowing each site to operate independently with its own processing capabilities.
2Adaptability or versatility
If data processing sites are disconnected from each other, then system independence and flexibility are improved, but metadata collection and lineage tracking become difficult
Solution Approach 1:
The central repository serves as an intermediary that simplifies metadata collection by providing a single point of aggregation. Each disconnected data processing site independently sends its metadata to the central repository, eliminating the need for complex peer-to-peer communication protocols and reducing the overall system complexity despite maintaining site independence.
Solution Approach 2:
Each data processing site autonomously generates and exports its own metadata without requiring intervention from other sites or complex coordination mechanisms. This self-service approach allows sites to maintain independence while contributing to the collective metadata store in the central repository, simplifying the overall metadata collection process.
3Measurement precision
If unique identifiers are generated for each data artifact, then data lineage tracking precision is improved, but system complexity increases due to identifier management
Solution Approach 1:
The patent combines the identifier generation and management functions into the centralized metadata repository. Instead of each data processing site independently managing its own identifiers, the system merges these functions at the central level, where identifiers are generated, stored, and managed in a unified manner. This reduces overall system complexity while maintaining high identification accuracy.
Data Source
AI summary
Systems and methods are provide for automatically constructing data lineage representations for distributed data processing pipelines. These data lineage representations (which are constructed and stored in a central repository shared by the multiple data processing sites) can be used to among other things, clone the distributed data processing pipeline for quality assurance or debugging purposes. Examples of the presently disclosed technology are able to construct data lineage representations for distributed data processing pipelines by (1) generating a hash content value for universally identifying each data artifact of the distributed data processing pipeline across the multiple processing stages/processing sites of the distributed data processing pipeline; and (2) creating an data processing pipeline abstraction hierarchy for associating each data artifact to input and output events for given executions of given data processing stages (performed by the multiple data processing sites).