Orchestrator Engine for Multi-Application Data Purge
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Solution Overview
Problem
Existing data purge methods in enterprise software applications fail to account for dynamic data dependencies across multiple applications, leading to data inconsistencies when deleting entities, especially in cloud-based systems where multiple applications share logical groups of data.
Innovation Solution
An orchestrator engine is introduced to manage entity deletions by validating deletion requests, identifying dependencies, and coordinating the deletion process across multiple applications using a publish-subscribe model, ensuring that all dependent entities are properly handled before proceeding with the deletion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing data purge methods are used in multi-application enterprise software systems, then deletion operations can be performed quickly and simply, but data inconsistencies occur due to unaccounted dynamic data dependencies across applications
Solution Approach 1:
An orchestrator component is introduced as an intermediary between applications and the data purge process. The orchestrator receives deletion requests, validates them by publishing validation messages to relevant applications, coordinates the identification of entity structures and dependent data entities, and manages the actual deletion. This intermediary handles the complexity of multi-application dependencies centrally, ensuring data integrity while maintaining a clean interface for deletion operations.
Solution Approach 2:
The system performs preliminary validation and identification actions before executing the actual deletion. The orchestrator first publishes validation messages to applications to verify deletion requests, then publishes identification messages to trace entity structures and dependent data entities across applications. Only after these preliminary steps confirm safety does the system proceed with the deletion, preventing data inconsistencies.
2Reliability
If validation and identification processes are performed across multiple applications before deletion, then data integrity is ensured, but the deletion process time increases
Solution Approach 1:
The validation and identification processes are implemented as continuous message-publishing operations rather than sequential batch processing. The orchestrator continuously publishes validation messages, identification messages, and review file requests to applications, maintaining an ongoing dialogue to trace entity structures and dependencies. This continuous approach efficiently coordinates across multiple applications without unnecessary pauses, reducing overall process time while ensuring data consistency.
3Loss of information
If the orchestrator coordinates deletion across all applications with multiple validation and identification steps, then complete data dependency tracking is achieved, but system complexity increases
Solution Approach 1:
The deletion coordination process is segmented into distinct, manageable phases: validation phase (publishing validation messages to verify deletion requests), identification phase (publishing identification messages to trace entity structures and dependent data entities), review phase (exchanging review files to confirm deletion safety), and execution phase (performing the actual deletion). Each phase has a specific purpose and can be independently implemented and tested, reducing overall system complexity while maintaining complete dependency tracking.
Data Source
AI summary
In some implementations, there is provided a method including validating, by an orchestrator, a deletion request by publishing a validation message to a plurality of applications including a first application and a second application, the validation message including an entity type and at least one criterion, the entity type associated with an entity structure of data entities. Related systems and articles of manufacture are also disclosed.