Schemaless to Relational Data Conversion System
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Solution Overview
Problem
Existing data representation technologies face inefficiencies in processing schemaless data, such as JSON, as they lack the flexibility of schemaless representations and the efficiency of relational databases, requiring manual processes like normalization or specialized storage formats that are time-consuming and inflexible.
Innovation Solution
An automated process that maps arbitrary JSON objects into a relational representation, allowing efficient storage and querying using columnar blocks in NoSQL or SQL databases, eliminating the need for manual schema declaration and reducing data ingestion time from months to hours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If schemaless data representation (JSON) is used, then flexibility with changes in data sources and structure is improved, but data processing efficiency deteriorates
Solution Approach 1:
The system dynamically adapts between two data representation modes: schemaless JSON format for flexible data ingestion and relational representation for efficient processing. The automatic conversion mechanism allows the system to switch between these modes based on operational requirements, maintaining flexibility while achieving processing efficiency.
Solution Approach 2:
An automatic conversion system acts as an intermediary between schemaless JSON data sources and relational database processing engines. This mediator automatically transforms JSON data into relational representation, enabling efficient processing without requiring manual intervention or schema declaration.
2Productivity
If manual normalization or specialized storage formats are used, then data processing efficiency is improved, but time consumption and operational complexity worsen
Solution Approach 1:
The conversion system operates autonomously without requiring manual normalization or specialized storage format configuration. It automatically performs the transformation from JSON to relational representation, eliminating time-consuming manual processes while maintaining processing efficiency.
Solution Approach 2:
The system performs automatic conversion of JSON data to relational representation as a preliminary step before data processing. This pre-processing action eliminates the need for subsequent manual normalization efforts, saving time and reducing operational complexity.
3Adaptability or versatility
If schemaless data representation is used, then flexibility is improved, but data processing efficiency deteriorates
Solution Approach 1:
An automatic conversion system serves as an intermediary that handles the complexity of transforming schemaless JSON data into relational representation. This mediator manages the conversion process transparently, maintaining flexibility in data sources while eliminating the need for users to deal with complex conversion procedures.
Solution Approach 2:
The system replaces manual mechanical processes of schema declaration and data normalization with an automated conversion mechanism. This substitution eliminates the complexity associated with manual intervention while preserving the flexibility benefits of schemaless data representation.
Data Source
AI summary
A system is disclosed. The system includes a processor configured to: receive a set of data structured in a schemaless data representation; automatically translate the set of data into a relational representation by: translating an array map value in the set of data into an ordered multi-map; and converting the ordered multi-map to the relational representation. The processor is further configured to store the translated set of data in a key-value data store for a query-based retrieval.