Stateful Data Integration with Periodic Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data integration systems face challenges in maintaining data integrity, particularly in stateless systems where data integrity is compromised over time due to errors, uncertainties, and conflicts arising from multiple integrations, leading to loss of confidence in data accuracy and consistency.
Innovation Solution
Implementing stateful integrations with a method that involves storing identifiers and metadata, performing integration actions, and conducting periodic validation stages to discover and correct errors, ensuring data integrity through auto-correction and post-validation processes, independent of the integration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stateless integration systems are used to move data between systems, then integration speed and simplicity are improved, but data integrity deteriorates over time due to errors, uncertainties, and conflicts
Solution Approach 1:
The system transitions from static stateless integrations to dynamic stateful integrations that adapt over time. Integration objects maintain state information about data quality, errors, and validation status, allowing the system to dynamically adjust monitoring and validation based on the current state of integrated data, thereby maintaining integrity without sacrificing speed
Solution Approach 2:
The system implements feedback mechanisms through periodic validation stages that assess data quality and integrity. Error detection and uncertainty identification feed back into the integration process, triggering auto-correction actions and alerts. This closed-loop feedback ensures data integrity is maintained over time while allowing rapid integrations to proceed
2Measurement precision
If monitoring is performed through log files or application interfaces during integration, then initial data quality can be verified, but monitoring becomes costly and resource-intensive over time as more integrations are added
Solution Approach 1:
Instead of monitoring all integrated data continuously with full validation, the system performs periodic validation at strategic intervals and only on specific data elements that are most critical or prone to errors. This partial monitoring approach maintains data quality verification while significantly reducing the complexity and cost of ongoing monitoring
Solution Approach 2:
The system introduces integration objects as intermediaries that store and track state information about integrated data. These objects serve as mediators between the integration process and validation/monitoring functions, allowing efficient tracking of data quality without requiring expensive continuous monitoring of all data flows
3Adaptability or versatility
If multiple integrations are implemented to move data across systems, then data coverage and system connectivity are improved, but data errors can become viral and corrupt other data in the target system
Solution Approach 1:
The system performs preliminary validation and error detection before data is fully integrated and before errors can propagate to other systems. By identifying uncertainties and errors early in the integration process and during periodic validation stages, the system takes preventive action to stop error propagation before it becomes viral across multiple integrations
Solution Approach 2:
The system implements feedback mechanisms that track the state of integrated data and alert when errors or uncertainties are detected. This feedback loop enables rapid response to prevent error propagation by triggering auto-correction actions or halting further integrations that would spread the error, thus protecting system connectivity while preventing harmful error propagation
Data Source
AI summary
A method including at least storing a plurality of identifiers and metadata associated with a plurality of integration actions. Each of the identifiers are associated with a readable object of a data source. Each of the integration actions are associated with one of the objects. The method including performing, during a first time period, the integration actions including creating objects, updating objects, or removing objects in a data target according to the corresponding objects of the data source. The method including performing, during a second time period, a discovery stage validation on readable objects of the data source and the data target to discover errors or uncertainties. The method also including performing, during a third time period, a re-validation related to the errors or uncertainties discovered in the discovery stage validation. The second and third time periods being independent of the first time period.