Dynamic Data Migration Auditing with Configurable Audit Points
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Solution Overview
Problem
Current ETL systems provide limited and static auditing of data migration, focusing on characterization rather than actual data content, and lack customization options for auditing data movement.
Innovation Solution
A dynamic auditing system that specifies audit points within a data migration path, collects data value statistics, and applies user-defined audit rules to generate metadata for real-time monitoring and validation, allowing for customizable and advanced rule application across multiple data flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static comparison methods are used to audit data migration, then the auditing process is simple and fast, but the information obtained is limited to organizational characteristics rather than actual data content
Solution Approach 1:
The system pre-defines multiple audit points within the data migration path and pre-configures various audit statistics types (checksums, data quality metrics, transformation validation). This preliminary setup enables comprehensive data content auditing without adding complexity during the actual migration execution, as the auditing framework is already in place to capture detailed data information at specified points.
Solution Approach 2:
The patent introduces an intermediary auditing layer that sits between the source and target systems, collecting and analyzing data content information without interfering with the migration process. This intermediary component captures detailed data characteristics, transformation accuracy, and content validity, providing comprehensive information about actual data content while maintaining system modularity and managing complexity.
2Adaptability or versatility
If basic checksum comparisons are used for data auditing, then the auditing tool is simple to implement, but it fails to provide robust information on actual data content and is not customizable
Solution Approach 1:
The auditing system is segmented into distinct components: multiple configurable audit points, various audit statistic types (checksums, data quality metrics, transformation validation), and customizable audit rules. Each segment can be independently configured and selected based on specific migration requirements, enabling high adaptability while managing complexity through modular design. Users can choose which audit points to monitor and which statistics to collect for each point.
Solution Approach 2:
The auditing system transitions from static, fixed configurations to dynamic, adaptable auditing. Users can dynamically select and configure audit points, choose different audit statistic types based on data characteristics, and apply customized audit rules that adapt to specific migration scenarios. This dynamic configuration capability provides versatility and adaptability while the system manages complexity through a flexible, parameter-driven architecture.
3Measurement precision
If comprehensive data content auditing is implemented, then detailed metadata is obtained, but the auditing process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary configuration of audit points and statistics types before data migration begins. Common audit patterns and rules are pre-defined and can be selected template-based, reducing setup time. During migration, the auditing process efficiently collects precise data content information at pre-specified points without adding significant time overhead, as the framework is already prepared to capture detailed metadata.
Solution Approach 2:
The system allows users to select only the necessary audit points and statistics types required for their specific migration needs, rather than auditing all possible data aspects. This partial action approach enables comprehensive auditing of critical data content areas while avoiding unnecessary auditing of less important aspects, thereby maintaining measurement precision for essential data values while reducing overall auditing time and resource consumption.
Data Source
AI summary
A computer readable medium includes executable instructions to audit data migration. The executable instructions include instructions to define a data migration path from a source to a target. Audit points are specified within the data migration path. Audit statistics to be generated at the audit points are identified. The audit statistics including data corresponding to individual data values transmitted over the data migration path. Audit rules to be applied against the audit statistics are established.