Metadata Extraction System for Big Data Lineage
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Solution Overview
Problem
The complexity and changing nature of metadata in big data sources from multiple upstream systems make it difficult to maintain accuracy and connectivity across systems, hindering the validation and understanding of downstream data.
Innovation Solution
A system that programmatically extracts metadata from big data sources, transforms it into a standard format, and loads it into a repository using a metadata management server, enabling detailed understanding of data lineage and connections across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If metadata is manually maintained across multiple upstream systems, then data accuracy and system connectivity can be preserved, but the complexity and time required to maintain metadata increases significantly
Solution Approach 1:
The system enables automatic extraction and maintenance of metadata from upstream systems through programmatic interfaces. The metadata management server autonomously connects to data sources, extracts metadata using defined schemas, transforms it to standard formats, and loads it into the repository without requiring manual intervention, thus maintaining data accuracy while eliminating time-consuming manual processes
Solution Approach 2:
The system performs preliminary extraction and standardization of metadata before it is needed for downstream data validation. By continuously maintaining an up-to-date metadata repository through automated processes, the system ensures that metadata is ready and available when required for data lineage tracking and validation activities
2Loss of information
If metadata from multiple upstream systems is integrated, then data lineage and connectivity understanding improves, but the complexity of managing diverse metadata formats increases
Solution Approach 1:
The metadata management server acts as an intermediary between diverse upstream systems and downstream consumers. It extracts metadata from various data sources using their specific interfaces, transforms all metadata to a standardized format using defined schemas, and stores it in a unified repository, thereby preserving complete data lineage information while simplifying management through standardization
Solution Approach 2:
The system transforms metadata from various formats and structures into a standardized format by changing parameters such as data types, naming conventions, and structural organization. This parameter transformation enables consistent representation of metadata from different upstream systems while maintaining the essential information needed for data lineage tracking
3Productivity
If automated metadata extraction is implemented, then metadata maintenance efficiency increases, but the initial system setup and configuration complexity increases
Solution Approach 1:
The automated metadata extraction system is segmented into distinct functional modules: extraction module that connects to upstream systems, transformation module that standardizes metadata using schemas, and loading module that stores metadata in the repository. This segmentation allows each component to be configured and maintained independently, reducing overall system complexity while enabling high automation efficiency
Data Source
AI summary
An example system for programmatically extracting data from a big data source includes: a processor; and system memory encoding instructions which, when executed by the processor, cause the system to: extract metadata from the big data source using the utility; transform the metadata into a standard format; and load the metadata in a repository.