Metadata-Driven Data Validation and Transformation System
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Solution Overview
Problem
Ingesting large volumes of big data sets is a costly and time-intensive process due to its manual and non-trivial nature, requiring significant user oversight and involving multiple steps with parallel processing, which increases data intake time and costs.
Innovation Solution
A metadata-driven system that receives source data with a metadata layer describing field types, lengths, and descriptions, validates the metadata, transforms source records into load-ready files, and balances records to generate a transformation failure rate, ensuring accurate and complete data transfer between systems, while enabling parallel processing and automated code creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data intake processes are used, then data integrity can be monitored, but data intake time and costs increase significantly
Solution Approach 1:
The system performs self-validation by automatically comparing source data against metadata definitions without requiring manual intervention. The processor validates data quality checks and transformation failures autonomously, generating reports and logs without user oversight for routine operations.
Solution Approach 2:
The system implements continuous feedback loops where transformation results are monitored, failure rates are calculated and compared against thresholds, and the system automatically adjusts or alerts based on performance. This enables real-time quality assurance without manual checking of each record.
2Productivity
If parallel processing is implemented, then processing speed increases, but data intake complexity increases
Solution Approach 1:
The system divides the data intake process into independent segments: metadata validation, data transformation, quality checking, and logging. Each segment can be processed in parallel by different processors or threads, reducing overall complexity while maintaining speed through modular architecture.
Solution Approach 2:
The metadata layer serves as an intermediary that simplifies the complexity of parallel processing by providing a standardized interface between source systems and target systems. This abstraction layer handles the complexity of data mapping and validation uniformly across parallel processing streams.
3Extent of automation
If automated code creation is used, then data intake automation increases, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining metadata schemas and validation rules before data intake begins. This allows the automated code generation to work with pre-established templates, reducing the complexity of creating transformation logic from scratch while maintaining high automation levels.
Data Source
AI summary
A system receives a source and a metadata layer that describes the source. The source may comprise source records with fields containing source data, and the metadata layer may include metadata comprising at least one of a field data type, a field data length, a field description, or a record length. The processor may further validate the metadata layer against the source and write results to a log. The processor may further be configured for transforming the source records into transformed records for a load ready file. The processor may further balance a number of records in the source against a number of transformed records in the load ready file to generate a transformation failure rate.