Data Management Request Proxy Engine for Automated Data Operations
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Solution Overview
Problem
Information management has become complex and labor-intensive due to increased data scale, volume, governance requirements, and process complexity, making it difficult for users to manage data effectively across different environments without impacting the main system.
Innovation Solution
A Data Management Request Proxy (DMRP) engine determines user intent and policies to create an abstract data management request, which is then processed to generate concrete requests using appropriate technologies, allowing for data movement or copying across various environments while adhering to constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data management operations are performed manually to maintain control and compliance, then data integrity and governance requirements are met, but the process becomes complex and labor-intensive
Solution Approach 1:
An automated data management engine is introduced as an intermediary between users and the core data system. This engine handles complex data management operations including copying, moving, and transforming data between different environments while automatically enforcing governance policies and constraints, thereby maintaining data integrity without requiring manual complex processes
Solution Approach 2:
The system enables self-service data management through automated operations that execute based on user-initiated requests. The data management engine autonomously performs data copying, movement, and transformation tasks while automatically complying with governance requirements, reducing the need for manual intervention and complex procedural management
2Ease of operation
If data is copied to independent environments for user access, then users can work independently without impacting the main system, but the data management process becomes more complex
Solution Approach 1:
The system segments data into different environmental copies (development, test, production, independent user environments) that can be independently managed. The automated engine handles the complexity of creating and maintaining these segmented environments, allowing users to work independently in their own data copies without impacting the main system while the system manages the complexity of synchronization and governance
3Productivity
If automated data management operations are implemented, then labor intensity is reduced and efficiency improves, but the skill level required for system configuration increases
Solution Approach 1:
The data management engine is designed as a universal system that handles multiple data management operations (copying, moving, transforming, synchronizing) across different environments through a single automated platform. This multi-functional engine reduces labor intensity by automating routine tasks while providing standardized interfaces that manage configuration complexity
Solution Approach 2:
The system manages complexity through parameter-based configuration where governance rules, policies, and constraints are defined as configurable parameters. The automated engine adjusts its behavior based on these parameters, allowing operational efficiency to improve through automation while configuration complexity is managed through standardized parameter settings rather than complex procedural knowledge
Data Source
AI summary
Provided are a method, computer program product, and system for processing a data management request. User intent that defines properties of target data is determined. Policies and constraints for the data management request are determined. An abstract data management request that identifies source data, the target data, and the polices and constraints is created. A technology to use to process the data management request based on the user intent, policies, and constraints is determined.