Metadata Lineage Tracing with Hierarchical Keys Across Enterprise Data Flows
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Solution Overview
Problem
Existing metadata management systems fail to provide comprehensive data lineage tracing across diverse enterprise systems, lacking the ability to interpret and track data transformations accurately, especially in complex environments with multiple programming languages and technologies.
Innovation Solution
An intelligent metadata management system that uses a hierarchical key to trace data lineage across enterprise systems, parsing source code to understand computing instructions and transformations, and displaying data lineage on a graphical user interface, with capabilities for time-based tracking and alerts on alterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive data lineage tracing is implemented across diverse enterprise systems, then data governance and compliance understanding is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent implements a universal metadata management system that can trace data lineage across diverse enterprise systems including mainframes, distributed systems, cloud platforms, and big data environments. The system uses a unified metadata repository and hierarchical key structure that works across all these different platforms, allowing one system to perform multiple tracing functions across heterogeneous environments without requiring separate solutions for each platform.
Solution Approach 2:
The patent introduces metadata as an intermediary layer between source systems and target systems. This metadata captures data lineage information, transformations, and relationships without requiring direct integration between all systems. The metadata repository acts as a mediator that stores and manages lineage information, allowing tracing across systems without complex point-to-point connections between each enterprise system.
2Measurement precision
If source code parsing is used to understand computing instructions and transformations, then data transformation tracking accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary parsing of source code to extract and store metadata about data transformations, computing instructions, and data flow relationships before actual data processing occurs. By pre-analyzing the source code and capturing transformation logic in advance, the system avoids the need to re-parse code during runtime, significantly reducing processing time while maintaining accurate transformation tracking.
Solution Approach 2:
The patent creates metadata copies of source code information that capture transformation logic without requiring the original source code to be present during data processing. These metadata copies contain essential information about data transformations, allowing the system to track transformations accurately by referencing the copied metadata rather than repeatedly analyzing the original source code.
3Measurement precision
If hierarchical keys are used to define and trace data elements across systems, then data element identification accuracy is improved, but metadata storage requirements increase
Solution Approach 1:
The patent divides the hierarchical key structure into multiple levels or segments that correspond to different layers of the enterprise architecture (e.g., mainframe level, distributed system level, cloud platform level, application level). Each segment of the hierarchical key identifies data elements at a specific layer, allowing precise identification without requiring a single monolithic key that would be excessively long and storage-intensive.
Solution Approach 2:
The patent implements a nested hierarchical key structure where keys at different levels are nested within each other. For example, a top-level key identifies a data element in the mainframe system, and nested within it are subordinate keys that identify the same element as it flows through distributed systems, cloud platforms, and applications. This nesting allows efficient storage by reusing parent key information rather than duplicating entire key paths at each level.
Data Source
AI summary
Disclosed herein are systems and methods for intelligent metadata management and data lineage tracing. In exemplary embodiments of the present disclosure, a data element can be traced throughout multiple applications, platforms, and technologies present in an enterprise to determine how and where the specific data element is utilized. The data element is traced via a hierarchical key that defines it using metadata. In this way, metadata is interpreted and used to trace data lineage from one end of an enterprise to another.