Data Domain Schema System for Enterprise Data Warehouse Compatibility
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Solution Overview
Problem
Existing data warehousing systems face issues with cross-domain uniformity, duplicative data storage, and contradictory messaging, leading to data compatibility problems, inefficient memory usage, and unnecessary resource waste.
Innovation Solution
The implementation of a data domain schema system that harmonizes data across an enterprise by transforming data metrics to conform to a uniform production schema, reducing duplicative data storage and ensuring accurate data publication through standardized schemas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from various sources is stored in a data warehouse with a single schema, then data can be collected from multiple sources, but data compatibility problems occur and source control of data metrics is lost
Solution Approach 1:
The patent segments the monolithic single-schema data warehouse into multiple data domains, each with its own schema. This allows data from various sources to be stored in domain-specific schemas while maintaining source control and compatibility. Each data domain acts as an independent segment that preserves the characteristics and metadata of its source data.
Solution Approach 2:
The patent applies local quality by allowing different data domains to have different schemas tailored to their specific requirements. Each data domain can maintain its own data structure, format, and metadata standards, enabling source-specific optimization while still being part of the overall data warehouse system.
2Quantity of substance
If data metrics are stored from multiple sources, then comprehensive data coverage is achieved, but duplicative data storage occurs consuming unnecessary memory space
Solution Approach 1:
The patent introduces data domain schemas as intermediaries between raw data sources and the central data warehouse. These intermediate schemas act as filtering and transformation layers that identify and eliminate duplicative data metrics before storage, reducing memory consumption while preserving comprehensive data coverage through intelligent data curation.
3Productivity
If data is stored without schema transformation, then storage speed is maintained, but cross-domain uniformity and integration of computing functionalities are compromised
Solution Approach 1:
The patent applies preliminary action by transforming data metrics to conform to domain-specific schemas before storage. This pre-transformation process ensures cross-domain uniformity and integration capability are established upfront, eliminating the need for subsequent data harmonization while maintaining efficient storage operations.
4Reliability
If duplicative data metrics are stored, then data redundancy is created, but search performance slows down and memory efficiency decreases
Solution Approach 1:
The patent extracts and removes duplicative data metrics from the storage system through schema-based deduplication. By using domain-specific schemas as filters, the system identifies and eliminates redundant copies of data metrics, keeping only essential unique data while maintaining adequate redundancy for reliability purposes.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for transforming data metrics to conform to a production schema for cross-domain uniformity. In particular, in one or more embodiments, the disclosed systems receive a metric having an initial schema, determine that the initial schema is inconsistent with a production schema, transform the metric to conform with the production schema, and store the metric in a standardized-schema database.