Data Lineage Mapping for Resource-Efficient Linkage Analysis
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Solution Overview
Problem
Managing large volumes of data in complex systems is resource-intensive and error-prone, with challenges in tracing errors, ensuring data compliance, and optimizing resource allocation.
Innovation Solution
A data processing system performs linkage analysis on datasets, generating a processed data representation that includes data lineage information and usage metrics, and provides visualizations to orchestrate data environments, thereby reducing manual inspection and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection and processing of large volumes of data is performed, then data management can be conducted, but computing resources and time are excessively consumed
Solution Approach 1:
The system performs preliminary actions by proactively mapping data environments and generating data lineage information before queries are executed. This includes pre-computing relationships between datasets and storing metadata about data sources, transformations, and dependencies, enabling faster query processing and reducing the need for manual inspection during actual data operations.
Solution Approach 2:
The system creates processed data representations that are simplified copies or abstractions of the actual complex data environments. These processed representations include aggregated metadata, data lineage summaries, and relationship mappings that allow users to analyze and manage data without directly interacting with the full complexity of the underlying datasets, thereby conserving computing resources.
2Reliability
If comprehensive data lineage tracking is implemented, then data compliance and error tracing are improved, but system complexity increases
Solution Approach 1:
The system segments data lineage tracking into manageable components by organizing metadata into structured hierarchies. Data lineage information is divided into discrete elements such as data source metadata, transformation metadata, and dependency metadata, each stored and processed independently. This segmentation allows the system to track comprehensive data relationships without overwhelming complexity, as each segment can be managed and queried separately.
3Loss of information
If detailed processed data representations are generated, then actionable insights are enhanced, but computing resources are consumed
Solution Approach 1:
The system applies partial action by generating processed data representations at different levels of detail based on specific query requirements. Rather than always creating fully detailed representations, the system generates only the necessary level of processing needed to answer each query, conserving computing resources while still providing actionable insights. This includes selectively aggregating data lineage information and relationship details based on the specific analytical needs.
Data Source
AI summary
In some implementations, a device may receive information identifying a set of data reports generated from a group of datasets. The device may request from a data source storing the group of datasets, and based on receiving the information identifying the set of data reports, source data associated with the set of data reports. The device may receive the source data associated with the set of datasets. The device may associate the source data with data lineage information identifying a set of connections between the group of datasets. The device may generate, based on associating the source data with the data lineage information, a processed data representation, wherein the processed data representation includes information identifying a set of relationships associated with the group of datasets, the set of data reports, and a set of usage metrics. The device may transmit information identifying the processed data representation.