Database Consolidation via Schema Normalization
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Solution Overview
Problem
The transition from legacy relational databases to cloud-based databases is inefficient due to the copying of database structures and content, which does not leverage cloud-based storage effectively and results in prolonged processes, especially when dealing with massive databases.
Innovation Solution
A method for consolidating multiple databases into a single or smaller number of databases by combining source tables into a target table, normalizing values, and generating conversion configuration files that use recursive operations to improve the structure and efficiency of the transition process, including error identification and correction during the creation of the target database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy databases are copied one-for-one to cloud-based databases, then database structure and content are preserved, but the transition process becomes protracted and cloud-based storage advantages are not leveraged
Solution Approach 1:
The patent consolidates multiple legacy databases into a single target database by merging their structures and data. Instead of copying each database separately, the system combines multiple source databases (with schemas like schema1, schema2, schema3) into one unified target database, eliminating redundant partitions and leveraging cloud-based storage capabilities effectively.
Solution Approach 2:
The patent creates a universal target database structure that can accommodate data from multiple different source databases with varying schemas. The conversion configuration file enables a single target database to serve multiple functions by integrating data from diverse sources, making the system adaptable to different legacy database structures.
2Reliability
If multiple massive databases are joined together, then complete data migration is achieved, but the joining operations grow exponentially and extend the transition process
Solution Approach 1:
The patent segments the database conversion process into manageable components by using a conversion configuration file that defines specific mapping relationships between source and target databases. The system divides the migration into schema-level conversions, table-level conversions, and data-level transformations, processing them in a structured sequence rather than attempting simultaneous joining of all databases.
Solution Approach 2:
The patent performs preliminary actions by first creating the conversion configuration file that outlines all mapping relationships before executing the actual data migration. The system pre-defines how source tables map to target tables, what transformations are needed, and the sequence of operations, which streamlines the subsequent joining process and prevents exponential complexity.
3Reliability
If conventional database partitioning is maintained in cloud-based environment, then legacy structure is preserved, but cloud-based storage advantages are not utilized
Solution Approach 1:
The patent inverts the conventional approach by not preserving legacy partitions in the cloud environment. Instead of copying the distributed partitioned structure to the cloud, the system consolidates multiple partitions into a unified cloud-based database structure, leveraging the cloud's inherent scalability and storage capabilities while maintaining data integrity through conversion rules.
4Manufacturing precision
If detailed conversion configuration is manually created, then accurate data mapping is achieved, but the generation process becomes complex and time-consuming
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate parts of the conversion configuration file by analyzing source database schemas and inferring mapping relationships. The system can autonomously identify table correspondences, data type transformations, and conversion rules, reducing manual configuration effort while maintaining mapping accuracy through automated validation.
Data Source
AI summary
A system, method, and computer-readable medium for consolidating multiple databases into a single or a smaller number of databases is described. An improved structure of target database may be achieved by removing constraints used when creating legacy databases. According to some aspects, these and other benefits may be achieved by combining multiple source tables into a single target table with errors being identified and addressed during the creation of the instructions to generate the target table. In implementation, this may be effected by converting values from a source table and values from a target table into normalized versions of those values and then comparing the normalized values. A benefit includes verifying that the data from the source table is being properly reflected in the target table.