Metadata Inheritance for Real-Time Data Ingestion Quality Control
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Solution Overview
Problem
Existing data processing systems face inefficiencies in data ingestion, with high latency, inaccuracies, and extensive code generation cycles due to the lack of metadata inheritance and attribution, leading to incomplete data cleansing and quality control.
Innovation Solution
A data processing system utilizing a graphical user interface for selecting data, determining metadata, and automatically processing data based on specified criteria, employing metadata inheritance to apply data quality controls and operations, such as masking PII, through a metadata model that links logical and technical metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual code generation and metadata management are used in data processing systems, then developers can implement custom data processing logic, but the system experiences high latency, extensive code generation cycles, and incomplete data cleansing due to lack of metadata inheritance
Solution Approach 1:
The system performs preliminary actions by pre-defining metadata models, data quality rules, and processing operations before actual data processing occurs. The metadata model is constructed in advance with inherited relationships, and data quality controls are predetermined based on logical metadata, eliminating the need for extensive manual code generation during runtime.
Solution Approach 2:
The system enables self-service by automatically generating processing instructions through metadata inheritance and traversal. When new data sources are added, the system automatically traverses the metadata model to identify applicable data quality rules and processing operations without requiring manual intervention or code generation, allowing the system to service itself.
2Reliability
If comprehensive data quality controls and metadata inheritance are implemented, then data processing accuracy and security are improved, but the system complexity increases due to metadata model construction and traversal
Solution Approach 1:
The system transitions from a flat, manual metadata management approach to a hierarchical, multi-dimensional metadata model with inheritance relationships. By organizing metadata in layers (logical metadata, technical metadata, data quality rules) with defined inheritance relationships, the system manages complexity through structural dimensionality rather than increasing operational complexity.
Solution Approach 2:
The metadata model serves as an intermediary layer between raw data and processing operations. Instead of directly managing complex processing logic for each data source, the system uses the metadata model as a mediator that automatically translates data source characteristics into appropriate processing instructions through traversal and inheritance, simplifying the overall system architecture.
3Manufacturing precision
If automated processing instructions are generated based on metadata traversal, then data cleansing and quality control are applied systematically, but the initial system setup and metadata model construction require significant effort
Solution Approach 1:
The metadata model and data quality rules are designed to be universal and applicable across multiple data sources and processing scenarios. A single metadata model construction can serve multiple data ingestion pipelines, and data quality rules defined once can be automatically applied to numerous data sources through inheritance, reducing repetitive setup effort while maintaining high cleansing precision.
Data Source
AI summary
A method implemented by a data processing system for enabling a system to pipeline or otherwise process data in conformance with specified criteria by providing a graphical user interface for selecting data to be processed, determining metadata of selected data, and, based on the metadata, automatically processing the selected data in conformance with the specified criteria.