Metadata-Driven Abstraction Layer for Multi-Tenant Cloud Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In multi-tenant cloud computing environments, changes to data schemas or object migrations across different computing platforms can lead to data loss, inefficient processes, and broken customizations due to environment-specific dependencies and connectors, making it difficult to manage data consistently across multiple systems.
Innovation Solution
A metadata-driven abstraction layer is implemented to abstract data object definitions, providing interface objects and fields that operate as abstracted versions of persistent objects and fields, allowing for dynamic mapping and transformation, thereby resolving conflicts and enabling transparent access across multiple computing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If environment-specific dependencies and connectors are implemented to manage data across multiple computing environments, then data can be accessed in each specific environment, but the system complexity increases and changes to one environment may break dependencies with other environments
Solution Approach 1:
The patent implements a universal data schema that can be used across multiple computing environments (Sales Cloud, Service Cloud, Marketing Cloud, etc.) without requiring environment-specific adaptations. This single unified schema replaces the need for multiple environment-specific schemas and connectors, reducing system complexity while maintaining the ability to access data across all environments.
Solution Approach 2:
The patent introduces an intermediary layer (the unified data schema with virtual fields) that mediates between different computing environments and their respective data sources. This intermediary abstracts the complexity of environment-specific connectors and dependencies, providing a consistent interface for data access while handling the complexity of cross-environment data relationships in the background.
2Ease of manufacture
If data schemas are updated or objects are migrated in one computing environment, then the data model can be improved, but existing dependencies and connectors with other environments may break causing data loss or broken process flows
Solution Approach 1:
The patent implements a dynamic data schema where virtual fields can be automatically created, updated, or removed based on the underlying data source changes. When data schemas are updated in any computing environment, the unified schema dynamically adapts by creating corresponding virtual fields to maintain the data model flexibility while preserving data integrity across all environments through automatic synchronization.
Solution Approach 2:
The patent employs a protective mechanism where the unified data schema pre-establishes virtual fields and relationships that cushion against schema changes in underlying environments. This beforehand cushioning ensures that when data schemas are updated or objects migrated in any environment, the unified schema maintains data integrity by having pre-configured virtual representations that prevent breakage of dependencies and connectors.
3Quantity of substance
If data for a single entity is spread across multiple computing environments, then comprehensive data storage is achieved, but accessing related information from different environments fails to recognize connections
Solution Approach 1:
The patent merges data from multiple computing environments into a unified data schema that combines records from Sales Cloud, Service Cloud, Marketing Cloud, and other environments into a single coherent data model. This merging allows comprehensive data storage while maintaining ease of access by presenting unified data relationships, enabling queries to traverse across environment boundaries and recognize connections between entities stored in different environments.
Solution Approach 2:
The patent adds a new dimensional layer (the unified data schema layer) above the existing multi-environment data structures. This dimensionality change allows data to be accessed both in its original environment-specific context and in the unified cross-environment context, enabling comprehensive data retrieval while maintaining efficient access through the additional abstraction dimension that recognizes relationships across environments.
Data Source
AI summary
Methods, systems, and devices supporting a data model abstraction framework are described. Some platforms (e.g., a multi-tenant cloud computing platform) may manage data across a number of persistent systems (e.g., cloud platforms, data stores, packages, etc.). To efficiently manage data across such systems, a platform may implement a metadata-driven abstraction layer. The abstraction layer may support a number of interface objects and interface fields, which may operate as abstracted versions of persistent objects and persistent fields. External entities (e.g., users, triggers, customizations, packages, plugins, or any other entities) may interact with the interface objects, and the abstraction layer may automatically handle mappings from the interface objects to the persistent systems. By using the metadata-driven abstraction layer, the platform may resolve conflicts between the external entities and multiple underlying persistent systems. Additionally, the abstraction layer may dynamically adjust mappings to handle migrations or other updates across the persistent systems.