Intelligent data migration system and method between different versions based on data platform

Abstract

The invention relates to the technical field of big data processing, in particular to an intelligent data migration system and method between different versions based on a data platform, and the system comprises a difference analysis module which is used for executing version difference analysis; the migration scheme generation module is used for generating a migration scheme; the migration execution module is used for executing the migration scheme and monitoring the migration process; the verification and optimization module is used for verifying the consistency of the migrated data, testing the functional integrity and optimizing the performance; the method has the beneficial effects that through automatic version difference analysis and migration scheme generation, the workload and time cost of manual operation are greatly reduced, and rapid migration of the data management platform is realized; strict data verification and error processing are carried out in the migration process, the integrity, accuracy and consistency of data are ensured, and data loss or damage is avoided.

Description

Technical Field

[0001] The present invention relates to the field of big data processing technology, and in particular to a system and method for intelligent data migration between different versions of a data platform. Background Art

[0002] In the digital evolution process, data governance platforms have become key tools for governments and businesses to manage and utilize data. As technology advances and business needs change, data governance platforms are constantly being updated and iterated, with frequent version upgrades or replacements.

[0003] However, different versions of data governance platforms have significant differences in data structure, functional modules, and configuration methods, making data migration difficult. Traditional migration methods often rely on manual operations, which are inefficient, error-prone, and difficult to ensure data integrity and accuracy, causing business interruptions and significant losses to enterprises.

[0004] Therefore, it is urgent to develop an intelligent, efficient and reliable data migration method for different versions of data governance platforms. Summary of the Invention

[0005] The purpose of the present invention is to provide a data intelligent migration system and method between different versions based on a data platform to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a data intelligent migration system between different versions of a data platform, comprising:

[0007] A difference analysis module, used to perform version difference analysis;

[0008] Migration plan generation module, used to generate migration plans;

[0009] Migration execution module, used to execute the migration plan and monitor the migration process;

[0010] Verification and optimization module, used for post-migration data consistency verification, functional integrity testing, and performance optimization.

[0011] Preferably, the difference analysis module includes a data structure analysis unit, a configuration file analysis unit and a function module analysis unit, which correspond to the difference identification functions of the data structure, the configuration file and the function module respectively.

[0012] Preferably, the migration solution generation module includes a data migration strategy formulation unit, a configuration migration solution planning unit and a function migration path planning unit, which are responsible for generating corresponding migration strategies, solutions and paths respectively.

[0013] Preferably, the migration execution module includes a data migration tool, a configuration migration script executor and a function migration deployer, as well as a migration monitoring subsystem to implement migration execution and monitoring functions.

[0014] Preferably, the verification and optimization module includes a data consistency verification unit, a functional integrity testing unit and a performance optimization adjustment unit to complete the verification and optimization work after migration.

[0015] A method for intelligently migrating data between different versions of a data platform includes the following steps:

[0016] Version difference analysis steps: Perform version difference analysis on the old and new versions of the data governance platform to identify differences in data structure, configuration files, and functional modules;

[0017] Migration plan generation step: Generate a personalized migration plan based on the difference analysis results, including data migration strategy, configuration migration plan, and function migration path;

[0018] Migration execution and monitoring steps: Execute the migration plan to achieve data, configuration, and function migration, and monitor the migration process;

[0019] Post-migration verification and optimization steps: Verification and optimization are performed after the migration is completed to ensure data consistency and functional integrity.

[0020] Preferably, in the version difference analysis step, data structure difference identification uses semantic analysis and pattern matching algorithms, configuration file difference comparison is performed by converting the configuration file into a unified format for node traversal and attribute comparison, and functional module difference evaluation uses code decompilation and static analysis tools to extract key features for comparison.

[0021] Preferably, in the migration plan generation step, the data migration strategy determines the data migration rules, batches and number of threads based on the data structure differences, the configuration migration plan generates an extensible script based on the configuration file differences, and the functional migration path formulates a deployment and integration plan based on the functional module difference evaluation results.

[0022] Preferably, in the migration execution and monitoring steps, data migration uses data migration tools for batch processing and cache optimization, configuration migration uses automated deployment tools to execute scripts and provide a rollback mechanism, functional migration uses containerization technology for deployment and joint debugging, and at the same time establishes a migration monitoring system for real-time monitoring and early warning.

[0023] Preferably, in the post-migration verification and optimization steps, data consistency verification is performed by writing verification scripts to compare key data, functional integrity testing is performed by performing multiple types of tests according to test cases, and performance optimization is performed by adjusting system parameters and optimizing functions based on monitoring data.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The intelligent data migration system and method between different versions of the data platform proposed in the present invention greatly reduces the workload and time cost of manual operations through automated version difference analysis and migration plan generation, and realizes the rapid migration of the data governance platform; strict data verification and error handling are performed during the migration process to ensure the integrity, accuracy and consistency of the data and avoid data loss or damage; reasonable migration plan planning and function migration path planning ensure the normal operation of the business during the migration process and reduce the impact on the enterprise business; comprehensive migration monitoring and early warning mechanism can timely discover and solve problems arising during the migration process, reduce migration risks, and improve the success rate of migration. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0027] In order to clearly and completely describe the objectives and technical solutions of the present invention and make the advantages more clearly understood, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, not all of them, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] In a first embodiment, the present invention provides a technical solution: a data intelligent migration system between different versions of a data platform, comprising:

[0029] The difference analysis module is used to perform version difference analysis; the difference analysis module includes a data structure analysis unit, a configuration file analysis unit and a function module analysis unit, which correspond to the difference identification functions of the data structure, configuration file and function module respectively.

[0030] The migration plan generation module is used to generate the migration plan. The migration plan generation module includes a data migration strategy formulation unit, a configuration migration plan planning unit, and a function migration path planning unit, which are responsible for generating the corresponding migration strategy, plan, and path respectively.

[0031] The migration execution module is used to execute the migration plan and monitor the migration process. The migration execution module includes a data migration tool, a configuration migration script executor, a function migration deployer, and a migration monitoring subsystem to implement migration execution and monitoring functions.

[0032] The verification and optimization module is used for data consistency verification, functional integrity testing, and performance optimization after migration. The verification and optimization module includes a data consistency verification unit, a functional integrity testing unit, and a performance optimization adjustment unit to complete the verification and optimization work after migration.

[0033] Example 2, based on Example 1, proposes a method for intelligent data migration system between different versions of a data platform, including the following steps:

[0034] (1) Version Difference Analysis

[0035] 1. Data Structure Difference Identification: By parsing the data dictionary and database table structure definition files between the old and new versions of the data governance platform, and utilizing semantic analysis and pattern matching algorithms, we automatically identify differences in data tables, fields, data types, primary keys, foreign keys, and other aspects. For example, we compare whether new data tables have been added to the new version, and whether certain fields in the old version have had their data types changed or have been deleted. For complex data structures, such as nested data and array-type data, we utilize recursive analysis algorithms for in-depth comparisons to ensure comprehensive difference identification. We implement intelligent switch identification for database types between the old and new versions, automatically adapting migration code based on the type.

[0036] 2. Configuration File Difference Comparison: Read the configuration files from the old and new versions and convert them into a unified, parseable format, such as JSON or XML. By traversing nodes and comparing attributes, we identify differences in configuration parameters, system parameters, user permissions, and other aspects. We also develop custom parsers to pre-process the configuration files for special syntax and formatting, preventing comparison errors caused by format differences.

[0037] 3. Functional Module Difference Assessment: Because each version involves multiple patches, before platform data migration, upgrade both the old and new versions of the data platform to the latest patch and back up the data on both the old and new versions. If the database is a High Go type, connect the relevant networks in advance. Analyze the code logic, interface definitions, and business processes of the new and old versions of the functional modules. Using code decompilation and static analysis tools, extract the key features of the functional modules, such as function call relationships, data flow, and input and output interfaces. By comparing these features, determine the addition, deletion, and modification of functional modules, as well as changes in functional dependencies.

[0038] (2) Migration plan generation

[0039] 1. Data migration strategy development: Develop data migration rules based on differences in data structures. For newly added fields, determine default values ​​or fill methods based on business logic and data relationships. For fields with changed data types, design data type conversion functions to ensure data accuracy and compatibility during the migration process. For tables with large data volumes, migrate them in batches and using multiple threads to improve migration efficiency and minimize business impact. Furthermore, consider data consistency and integrity, perform data validation and error handling during the migration process, and promptly record any data conflicts or format errors, and provide business remediation suggestions.

[0040] 2. Generate a configuration migration script based on configuration file differences. For newly added configuration parameters, perform initial configuration according to the platform's default settings and best practices. For modified configuration parameters, adjust them based on the requirements of the new version and business needs. For user permission configuration, combine the company's organizational structure and permission management policy to ensure that user permissions in the new version of the platform remain consistent with the old version or are reasonably upgraded. Configuration migration scripts are written in an extensible scripting language, such as Python or Shell, and are customized according to different platform versions and enterprise needs.

[0041] 3. Plan the functional migration path based on the functional module difference assessment results. For newly added functional modules, develop deployment and integration plans to ensure their compatibility and interoperability with existing systems. For modified functional modules, analyze their impact on upstream and downstream systems and develop corresponding interface adaptation and functional testing plans. For deleted functional modules, evaluate their impact on business processes and provide alternative solutions or data migration strategies to ensure business continuity. Functional migration path planning uses project management tools for visual management, clarifying the tasks, responsible individuals, timelines, and deliverables at each stage.

[0042] (3) Migration Execution and Monitoring

[0043] 1. Data migration execution: Develop data migration tools to implement data migration from the old version platform to the new version platform according to the established data migration strategy. During the migration process, Redis data caching and batch asynchronous processing technology are used to reduce database I / O operations and increase migration speed. At the same time, the migration progress and data status, such as the amount of data migrated and data records of failed migrations, are recorded in real time to facilitate user monitoring and management. The data migration tool supports a variety of data sources and target databases, such as relational databases (MySQL, Oracle, highgo) and non-relational databases (MongoDB, Redis), and is expandable through plug-ins to meet the needs of different data governance platforms.

[0044] 2. Configuration Migration Implementation: Execute the dbemport script to export the data and configuration from the old version and generate a tarball of the old version's data. This migrates the data and configuration information from the old platform to the new version. During the migration process, the import script and check code verify the validity of configuration parameters to ensure configuration correctness and integrity. A transaction rollback mechanism is also provided to quickly restore the old version's configuration if any issues arise during the migration. Automated deployment tools, such as Ansible, are used to implement batch deployment and remote management of configurations.

[0045] 3. Functional Migration Deployment: Deploy and integrate new versions of functional modules according to the planned functional migration path. During the deployment process, functional testing and performance optimization are conducted to ensure the proper operation of the functional modules and that performance indicators meet business requirements. Simultaneously, interface debugging is performed between upstream and downstream systems to ensure correct data transmission and smooth integration of business processes. Functional migration deployment utilizes containerization technologies, such as Docker and Kubernetes, to enable rapid deployment, isolation, and scalability of functional modules.

[0046] 4. Migration Monitoring and Alerts: Establish a migration monitoring system to monitor the progress, status, and performance indicators of data, configuration, and function migrations in real time. By setting thresholds and alert rules, administrators will receive timely alerts when abnormalities occur during the migration process, such as slow migration speed, data verification failures, or functional test failures. The migration monitoring system uses a visual interface to display migration status, facilitating real-time monitoring and decision-making by administrators.

[0047] (4) Post-migration verification and optimization

[0048] 1. Data consistency verification: After the migration is complete, perform a comprehensive data consistency verification. By writing a data verification script, compare the integrity, accuracy, and consistency of key data between the old and new versions of the platform. For example, check whether the number of data records is consistent, whether the values ​​of key fields match, and whether data relationships are correct. For any data inconsistencies found, provide detailed error reports and remediation suggestions to help administrators quickly locate and resolve issues.

[0049] 2. Functional Integrity Testing: Comprehensively test the new platform's functionality to ensure completeness and usability. Functional, performance, and security testing are performed according to the business requirements and test cases of the functional modules. Any functional defects and performance issues discovered are promptly fixed and optimized to ensure the new platform meets business requirements.

[0050] 3. Performance Optimization and Adjustment: Based on post-migration performance monitoring data, we optimized the performance of the new platform. For example, we adjusted database indexes, optimized query statements, and adjusted system configuration parameters to improve the platform's responsiveness and processing capabilities. We also collected user feedback to optimize the platform's functionality and interface to enhance the user experience.

[0051] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A data intelligent migration system between different versions of a data platform, characterized by: include: A difference analysis module, used to perform version difference analysis; Migration plan generation module, used to generate migration plans; Migration execution module, used to execute the migration plan and monitor the migration process; Verification and optimization module, used for post-migration data consistency verification, functional integrity testing, and performance optimization.

2. The data intelligent migration system between different versions of a data platform according to claim 1, characterized in that: The difference analysis module includes a data structure analysis unit, a configuration file analysis unit and a function module analysis unit, which correspond to the difference identification functions of the data structure, configuration file and function module respectively.

3. The intelligent data migration system between different versions of a data platform according to claim 2, characterized in that: The migration plan generation module includes a data migration strategy formulation unit, a configuration migration plan planning unit, and a function migration path planning unit, which are responsible for generating corresponding migration strategies, plans, and paths respectively.

4. The intelligent data migration system between different versions of a data platform according to claim 3, characterized in that: The migration execution module includes a data migration tool, a configuration migration script executor, a function migration deployer, and a migration monitoring subsystem to implement migration execution and monitoring functions.

5. The intelligent data migration system between different versions of a data platform according to claim 4, characterized in that: The verification and optimization module includes a data consistency verification unit, a functional integrity test unit, and a performance optimization adjustment unit to complete post-migration verification and optimization work.

6. A method for intelligent data migration between different versions of a data platform according to claim 5, characterized in that: The following steps are involved: Version difference analysis steps: Perform version difference analysis on the old and new versions of the data governance platform to identify differences in data structure, configuration files, and functional modules; Migration plan generation step: Generate a personalized migration plan based on the difference analysis results, including data migration strategy, configuration migration plan, and function migration path; Migration execution and monitoring steps: Execute the migration plan to achieve data, configuration, and function migration, and monitor the migration process; Post-migration verification and optimization steps: Verification and optimization are performed after the migration is completed to ensure data consistency and functional integrity.

7. A method according to claim 6, characterized in that: In the version difference analysis step, semantic analysis and pattern matching algorithms are used to identify data structure differences. Configuration file difference comparison is performed by converting the configuration files into a unified format for node traversal and attribute comparison. Functional module difference assessment uses code decompilation and static analysis tools to extract key features for comparison.

8. A method according to claim 6, characterized in that: In the migration plan generation step, the data migration strategy determines the data migration rules, batches, and number of threads based on data structure differences. The configuration migration plan generates extensible scripts based on configuration file differences. The functional migration path formulates deployment and integration plans based on the functional module difference assessment results.

9. A method according to claim 6, characterized in that: During the migration execution and monitoring steps, data migration uses data migration tools for batch processing and cache optimization. Configuration migration uses automated deployment tools to execute scripts and provide a rollback mechanism. Functional migration uses containerization technology for deployment and joint debugging. At the same time, a migration monitoring system is established for real-time monitoring and early warning.

10. A method according to claim 6, characterized in that: In the post-migration verification and optimization steps, data consistency verification is carried out by writing verification scripts to compare key data, functional integrity testing is carried out by performing multiple types of tests according to test cases, and performance optimization is carried out by adjusting system parameters and optimizing functions based on monitoring data.

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