Common Data Platform Object-Relational Mapping Across Frameworks
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Solution Overview
Problem
Existing data platforms struggle to provide rich data access and manipulation services integrated with programming environments, especially in multi-tier application architectures handling structured, semi-structured, and unstructured data, and fail to bridge the gap between different application frameworks.
Innovation Solution
A common data platform (CDP) architecture that maps tables to objects, providing a unified data storage component for structured, semi-structured, and unstructured data, with an API for object-relational mapping, query mapping, and constraint/security enforcement, enabling data sharing across disparate application frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is manipulated in multiple tiers with memory-resident data, then data access speed is improved, but data consistency and synchronization across tiers become more difficult
Solution Approach 1:
The platform implements feedback mechanisms through events and notifications that propagate data changes across all tiers. When data is modified in any tier, the system generates notifications that trigger appropriate synchronization actions, ensuring all tiers remain consistent without requiring constant polling or manual synchronization.
Solution Approach 2:
The platform establishes data access patterns and synchronization protocols in advance through the defined data model. Change tracking and event propagation mechanisms are pre-configured to handle multi-tier data operations, so synchronization actions are automatically initiated when changes occur, rather than requiring reactive coordination.
2Ease of operation
If object-relational mapping is implemented to bridge application objects and database tables, then ease of data manipulation is improved, but complexity of the data platform architecture increases
Solution Approach 1:
The platform extracts the object-relational mapping functionality into a dedicated middle tier that handles all mapping concerns centrally. This separates the mapping complexity from individual applications, allowing them to work with pure objects while the platform layer manages the translation to and from relational storage structures.
3Adaptability or versatility
If a unified data model is created to support multiple frameworks, then data sharing across frameworks is improved, but the complexity of supporting diverse data types increases
Solution Approach 1:
The unified data model is segmented into hierarchical levels: a core framework-agnostic data structure at the foundation, with framework-specific extensions and specializations built layer by layer. This allows the platform to support diverse data types (objects, files, XML) through a structured hierarchy rather than a monolithic complex model.
Data Source
AI summary
Data management between a common data store and multiple applications of multiple disparate application frameworks. A data storage component is provided that facilitates the storage of data, which data includes structured, semi-structured, and unstructured data. A common data platform interfaces to the data storage component to provide data services accessible by a plurality of disparate application frameworks, which data services allow a corresponding application of the different frameworks to access the data.