Data migration and integration method based on distributed cloud platform

Abstract

The invention particularly relates to a data migration and integration method based on a distributed cloud platform. The data migration and integration method based on the distributed cloud platform comprises the following steps: firstly, determining a migration range and a target, formulating a data migration strategy, building a migration environment, and configuring a security strategy; then, data extraction and marking are carried out, a metadata management system is constructed, data cleaning is carried out according to business rules, and data model mapping and conversion are carried out according to requirements of a target system; and customizing and selecting a data transmission protocol, performing data adaptation and conversion work according to the data structure of the target system, and generating a migration report after the data is stored in the target storage system. According to the data migration and integration method based on the distributed cloud platform, automatic and batch processing of the data is achieved, the data processing efficiency is improved, the safety of the data in the migration and integration process is guaranteed, the tracking, query and management capabilities of the data are enhanced, and the performance and response speed of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing and big data, and in particular to a data migration and integration method based on a distributed cloud platform. Background Art

[0002] With the rapid development of information technology, big data has become one of the core resources of today's era. As an effective tool for processing large-scale data, the distributed cloud platform has penetrated into all walks of life.

[0003] A distributed cloud platform consists of cloud services distributed in multiple physical locations, connected through a network and providing elastic and scalable computing, storage and data processing capabilities. It has the advantages of high availability, scalability, flexibility and cost-effectiveness, making it an ideal choice for organizational data migration and integration.

[0004] Data migration technologies include full migration and incremental migration. Full migration is a method of migrating all data in the source system to the target platform at one time. It is suitable for scenarios with small data volumes or high requirements for data consistency. Full migration can ensure that the data in the source and target systems are completely consistent, but it may require a longer migration time and a larger storage space. Incremental migration is a method of migrating only the data that has changed since the last migration. It is suitable for scenarios with large data volumes or high requirements for data real-time performance. Incremental migration can reduce migration time and storage space, but it is necessary to ensure that the incremental data is consistent with the data in the source system.

[0005] Data integration technology includes ETL (extract, transform, load) tools and data integration platforms. ETL tools are used to extract data from different data sources, transform and clean it, and then load it to the target platform. The data integration platform provides a unified view and access mechanism to integrate data from different sources to achieve data consistency and accessibility. ETL tools play a key role in the data integration process. It can extract data from different data sources, convert the data into a unified format and structure, and clean and verify it. ETL tools provide a visual operation interface that enables data integrators to easily define and execute data transformation logic. The data integration platform provides higher data integration capabilities. It not only supports the ETL process, but also provides functions such as data quality management, data warehouse management, and data exchange. The data integration platform can help organizations build complex data integration logic and achieve large-scale data integration and collaboration.

[0006] Traditional data migration requires a large number of physical devices and network resources. In order to improve the efficiency of data migration while reducing the cost of data migration, the present invention proposes a data migration and integration method based on a distributed cloud platform. Summary of the invention

[0007] In order to overcome the deficiencies of the prior art, the present invention provides a simple and efficient data migration and integration method based on a distributed cloud platform.

[0008] The present invention is achieved through the following technical solutions:

[0009] A data migration and integration method based on a distributed cloud platform includes the following steps:

[0010] Step S1: Demand Analysis and Planning

[0011] First, determine the scope and objectives of the migration and list the types of data that need to be migrated. At the same time, evaluate the compatibility of existing applications, databases, and storage systems, clarify business continuity requirements after data migration, and develop a data migration strategy based on business needs and resource conditions.

[0012] Step S2: Build the migration environment and configure security policies

[0013] On the distributed cloud platform, select services and configurations based on business needs, including but not limited to computing instance specifications, storage types and capacity, and network bandwidth; deploy Docker containers and migration tools while ensuring the stability and scalability of the environment;

[0014] Security strategies include data transmission layer encryption, static data encryption, access control and authentication mechanisms. At the same time, data backup and recovery strategies are formulated to ensure that data is not leaked or damaged during the migration process.

[0015] Step S3: Data source access and metadata management

[0016] Use ETL tools to extract data and add metadata to the data version, source, and extraction time information to facilitate tracking and management;

[0017] Build a metadata management system to record the structure, type and relationship of data, support data tracking, query and verification, and use metadata management to achieve data governance and compliance requirements;

[0018] Step S4: Data cleaning and conversion

[0019] Clean the data according to business rules, and deal with inconsistencies, missing values, and outliers in the data by writing scripts or using data cleaning tools;

[0020] Perform data model mapping and conversion according to the requirements of the target system, including field mapping and data format conversion;

[0021] Step S5: Data transmission and scheduling

[0022] Customize and select data transmission protocols, and formulate batch transmission or concurrent transmission strategies based on network bandwidth and data volume to ensure efficient and stable data transmission;

[0023] Step S6: Target system adaptation and data storage

[0024] Adapt and convert data according to the data structure of the target system, including rearranging data fields and converting data types. Generate migration reports through the management interface and provide migration status and task management functions.

[0025] Store the converted and adapted data in the target storage system to ensure data persistence and availability;

[0026] Step S7: Monitoring and management

[0027] Generate a migration report through the management interface, which includes the migration status, progress, problems encountered and solutions; at the same time, it provides task management functions to realize the monitoring and scheduling management of migration tasks;

[0028] Step S8: Testing and Optimization

[0029] Conduct integrity, accuracy and performance tests on migrated data to ensure data correctness and stable system operation;

[0030] Based on the test results, optimize the migration and integration process, including improving data cleaning and conversion processes and optimizing data transmission strategies to improve migration efficiency and data quality;

[0031] Regularly maintain and upgrade the migration and integration system, fix known vulnerabilities, and improve system stability and security; at the same time, pay attention to business development and technological progress, and adjust the migration and integration strategies in a timely manner.

[0032] In step S1, the data types migrated include structured data, semi-structured data and unstructured data;

[0033] When determining the migration window, avoid business peak periods and reserve a rollback plan in case of migration failure.

[0034] In the step S1, when formulating the incremental data migration strategy, the data synchronization mechanism is refined, including the capture, temporary storage and transmission mechanism of data changes.

[0035] In step S5, the data is compressed before transmission to reduce the amount and time of data transmission; the data transmission layer encryption and static data encryption technology are combined to ensure the security of the data during transmission.

[0036] In step S6, when storing relational data, the database and index are rebuilt.

[0037] The data migration and integration system based on the distributed cloud platform includes source data identification and access layer, data processing layer, data transmission layer, target data adaptation and storage layer, monitoring and management workstation and security tools;

[0038] The source data identification and access layer is responsible for accessing the data source, identifying the data source and initially extracting data through the adapter; and marking the extracted data with metadata, including data source information, data structure and data type, to provide a basis for data migration and integration.

[0039] The data processing layer is responsible for cleaning the extracted data, including removing duplicate data, processing missing values ​​and converting data formats to ensure data quality; converting data according to business needs, including data aggregation, splitting and calculation to adapt to the data structure of the target system; encrypting sensitive data to ensure data security during migration;

[0040] The data transmission layer is responsible for adopting the transmission protocol to transmit data and ensure the integrity of data during transmission; compressing data during transmission to reduce network bandwidth consumption and improve transmission efficiency; realizing intelligent transmission scheduling and dynamically adjusting the transmission strategy according to network conditions and data volume;

[0041] The target data adaptation and storage layer is responsible for the final step of data adaptation according to the data structure of the target system to ensure that the data can be seamlessly integrated into the target system; and stores the migrated and integrated data in the target data storage system;

[0042] The monitoring and management workstation is responsible for real-time monitoring of the data migration and integration process, including data flow, transmission speed and error handling, to ensure the traceability and manageability of the migration process; it provides a user interface to enable the administrator to monitor the migration status, manage migration tasks, and generate migration reports;

[0043] Security Tools: Responsible for implementing security policies including access control, authentication and authorization, protecting data from unauthorized access, and ensuring that data migration and integration processes comply with relevant industry standards.

[0044] A data migration and integration device based on a distributed cloud platform, characterized in that it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above-mentioned method steps when executing the computer program.

[0045] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program implements the above method steps when executed by a processor.

[0046] The beneficial effects of the present invention are as follows: the data migration and integration method based on the distributed cloud platform not only realizes the automation and batch processing of data and improves the data processing efficiency, but also ensures the security of data during the migration and integration process, enhances the ability to track, query and manage data, and improves the performance and response speed of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Attached Figure 1 It is a schematic diagram of the data migration and integration method based on the distributed cloud platform of the present invention.

[0049] Attached Figure 2 A schematic diagram of a migration environment preparation method according to the present invention. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0051] As attached Figure 1 As shown, the data migration and integration method based on the distributed cloud platform includes the following steps:

[0052] Step S1: Demand Analysis and Planning

[0053] First, determine the scope and objectives of the migration, and list in detail the types of data to be migrated; at the same time, evaluate the compatibility of existing applications, databases, and storage systems, and clarify the business continuity requirements after data migration, such as service uninterrupted time, data consistency and accuracy standards, etc. Develop a data migration strategy based on business needs and resource conditions.

[0054] In step S1, the data types migrated include structured data, such as MYSQL database, semi-structured data and unstructured data;

[0055] When determining the migration window, avoid business peak periods and reserve sufficient rollback plans to deal with migration failures.

[0056] In the step S1, when formulating the incremental data migration strategy, the data synchronization mechanism is refined, including the capture, temporary storage and transmission mechanism of data changes.

[0057] Step S2: Build the migration environment and configure security policies

[0058] On the distributed cloud platform, select appropriate services and configurations based on business needs, including but not limited to computing instance specifications, storage types and capacity, and network bandwidth; deploy Docker containers and migration tools, such as using database version control and migration tool Flyway for MySQL database migration, while ensuring the stability and scalability of the environment;

[0059] Security strategies include data transmission layer encryption (such as TLS), static data encryption (such as AES), strict access control and authentication mechanisms (such as OAuth 2.0), and data backup and recovery strategies to ensure that data is not leaked or damaged during the migration process;

[0060] Step S3: Data source access and metadata management

[0061] Use professional ETL (Extract, Transform, Load) tools to extract data, and add metadata to the data version, source, and extraction time information to facilitate tracking and management;

[0062] Build a metadata management system to record the structure, type and relationship of data in detail, and support data tracking, query and verification; metadata management can also be used to achieve data governance and compliance requirements.

[0063] Step S4: Data cleaning and conversion

[0064] Perform data cleaning according to business rules, and handle inconsistencies, missing values, and outliers in the data by writing scripts or using data cleaning tools; sufficient logs should be retained during the cleaning process for subsequent audits and problem tracking.

[0065] Perform data model mapping and conversion according to the requirements of the target system, including field mapping and data format conversion; for complex data conversion, it may be necessary to write customized conversion scripts.

[0066] Step S5: Data transmission and scheduling

[0067] Customize and select efficient and stable data transmission protocols, such as FTP, SFTP, HTTP, etc. Develop batch transmission or concurrent transmission strategies based on network bandwidth and data volume to ensure efficient and stable data transmission;

[0068] In step S5, the data is compressed before transmission to reduce the amount and time of data transmission; the data transmission layer encryption and static data encryption technology are combined to ensure the security of the data during transmission.

[0069] Step S6: Target system adaptation and data storage

[0070] Adapt and convert data according to the data structure of the target system, including rearranging data fields and converting data types. Generate migration reports through the management interface and provide migration status and task management functions.

[0071] Store the converted and adapted data in the target storage system to ensure data persistence and availability;

[0072] In step S6, when storing relational data, the database and index are rebuilt.

[0073] Step S7: Monitoring and management

[0074] Generate a detailed migration report through the management interface, which includes the migration status, progress, problems encountered and solutions; at the same time, provide task management functions to realize the monitoring and scheduling management of migration tasks;

[0075] Step S8: Testing and Optimization

[0076] Conduct integrity, accuracy and performance tests on migrated data to ensure data correctness and stable system operation;

[0077] Based on the test results, optimize the migration and integration process, including improving data cleaning and conversion processes and optimizing data transmission strategies to improve migration efficiency and data quality;

[0078] Regularly maintain and upgrade the migration and integration system, fix known vulnerabilities, and improve system stability and security; at the same time, pay attention to business development and technological progress, and adjust the migration and integration strategies in a timely manner.

[0079] The data migration and integration system based on the distributed cloud platform includes source data identification and access layer, data processing layer, data transmission layer, target data adaptation and storage layer, monitoring and management workstation and security tools;

[0080] The source data identification and access layer is responsible for accessing different data sources (such as relational databases, non-relational databases, file systems, etc.), identifying data sources and extracting data preliminarily through adapters; and marking the extracted data with metadata, including data source information, data structure, and data type, to provide a basis for data migration and integration.

[0081] The data processing layer is responsible for cleaning the extracted data, including removing duplicate data, processing missing values ​​and converting data formats to ensure data quality; converting data according to business needs, including data aggregation, splitting and calculation to adapt to the data structure of the target system; encrypting sensitive data to ensure data security during migration;

[0082] The data transmission layer is responsible for using reliable transmission protocols (such as HTTPS, FTP, etc.) for data transmission to ensure the integrity of data during transmission; compressing data during transmission to reduce network bandwidth consumption and improve transmission efficiency; realizing intelligent transmission scheduling and dynamically adjusting transmission strategies according to network conditions and data volume;

[0083] The target data adaptation and storage layer is responsible for the final step of data adaptation according to the data structure of the target system to ensure that the data can be seamlessly integrated into the target system; the migrated and integrated data is stored in the target data storage system, such as a relational database, object storage, distributed file system, etc.

[0084] The monitoring and management workstation is responsible for real-time monitoring of the data migration and integration process, including data flow, transmission speed and error handling, to ensure the traceability and manageability of the migration process; it provides a user interface to enable the administrator to monitor the migration status, manage migration tasks, and generate migration reports;

[0085] Security Tools: Responsible for implementing security policies including access control, authentication and authorization, protecting data from unauthorized access, and ensuring that data migration and integration processes comply with relevant industry standards.

[0086] The entire architecture design supports automation, batch processing, and real-time data stream processing to meet the needs of data migration and integration in different scenarios. At the same time, it has good scalability and can expand with the growth of data volume and business needs.

[0087] The data migration and integration device based on a distributed cloud platform includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above-mentioned method steps when executing the computer program.

[0088] The readable storage medium stores a computer program, and when the computer program is executed by a processor, the above method steps are implemented.

[0089] This data migration and integration method based on a distributed cloud platform can effectively improve data processing efficiency, reduce manual operations, and realize automatic and batch processing of data through professional data migration and integration strategies; by implementing encryption, access control and other security measures during the data migration process, it can ensure the security of data during transmission and storage, and prevent data leakage or illegal access; through data cleaning and conversion, it can improve data quality; establish a metadata management system to enhance the ability to track, query and manage data, and facilitate subsequent data analysis and application; through the reasonable allocation and scheduling of cloud computing resources, optimize the utilization of computing and storage resources, and improve system performance and response speed.

[0090] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A data migration and integration method based on a distributed cloud platform, characterized in that: The following steps are involved: Step S1: Demand Analysis and Planning First, determine the migration scope and objectives, and list the types of data to be migrated. At the same time, evaluate the compatibility of existing applications, databases, and storage systems, clarify the business continuity requirements after data migration, and formulate a data migration strategy based on business needs and resource conditions. Step S2: Build the migration environment and configure security policies On the distributed cloud platform, select services and configurations based on business needs, including but not limited to computing instance specifications, storage types and capacity, and network bandwidth; deploy Docker containers and migration tools while ensuring the stability and scalability of the environment; Security strategies include data transmission layer encryption, static data encryption, access control and authentication mechanisms. At the same time, data backup and recovery strategies are formulated to ensure that data is not leaked or damaged during the migration process. Step S3: Data source access and metadata management Use ETL tools to extract data and add metadata to the data version, source, and extraction time information to facilitate tracking and management; Build a metadata management system to record the structure, type and relationship of data, support data tracking, query and verification, and use metadata management to achieve data governance and compliance requirements; Step S4: Data cleaning and conversion Clean the data according to business rules, and deal with inconsistencies, missing values, and outliers in the data by writing scripts or using data cleaning tools; Perform data model mapping and conversion according to the requirements of the target system, including field mapping and data format conversion; Step S5: Data transmission and scheduling Customize and select data transmission protocols, and formulate batch transmission or concurrent transmission strategies based on network bandwidth and data volume to ensure efficient and stable data transmission; Step S6: Target system adaptation and data storage Adapt and convert data according to the data structure of the target system, including rearranging data fields and converting data types. Generate migration reports through the management interface and provide migration status and task management functions. Store the converted and adapted data in the target storage system to ensure data persistence and availability; Step S7: Monitoring and management Generate a migration report through the management interface, which includes the migration status, progress, problems encountered and solutions; at the same time, it provides task management functions to realize the monitoring and scheduling management of migration tasks; Step S8: Testing and Optimization Conduct integrity, accuracy and performance tests on migrated data to ensure data correctness and stable system operation; Based on the test results, optimize the migration and integration process, including improving data cleaning and conversion processes and optimizing data transmission strategies to improve migration efficiency and data quality; Regularly maintain and upgrade the migration and integration system, fix known vulnerabilities, and improve system stability and security; at the same time, pay attention to business development and technological progress, and adjust the migration and integration strategies in a timely manner.

2. The data migration and integration method based on a distributed cloud platform according to claim 1, characterized in that: In step S1, the data types migrated include structured data, semi-structured data and unstructured data; When determining the migration window, avoid business peak periods and reserve a rollback plan in case of migration failure.

3. The data migration and integration method based on a distributed cloud platform according to claim 2 is characterized in that: In the step S1, when formulating the incremental data migration strategy, the data synchronization mechanism is refined, including the capture, temporary storage and transmission mechanism of data changes.

4. The data migration and integration method based on a distributed cloud platform according to claim 1, characterized in that: In step S5, the data is compressed before transmission to reduce the amount and time of data transmission; the data transmission layer encryption and static data encryption technology are combined to ensure the security of the data during transmission.

5. The data migration and integration method based on a distributed cloud platform according to claim 1 is characterized in that: In step S6, when storing relational data, the database and index are rebuilt.

6. A data migration and integration system based on a distributed cloud platform, characterized in that: It includes source data identification and access layer, data processing layer, data transmission layer, target data adaptation and storage layer, monitoring and management workstation and security tools; The source data identification and access layer is responsible for accessing the data source, identifying the data source and initially extracting data through the adapter; Metadata tagging of extracted data, including data source information, data structure and data type, to provide a basis for data migration and integration; The data processing layer is responsible for cleaning the extracted data, including removing duplicate data, processing missing values ​​and converting data formats to ensure data quality; converting data according to business needs, including data aggregation, splitting and calculation to adapt to the data structure of the target system; Encrypt sensitive data to ensure data security during migration; The data transmission layer is responsible for adopting the transmission protocol to transmit data and ensure the integrity of the data during the transmission process; Compress data during transmission to reduce network bandwidth consumption and improve transmission efficiency; realize intelligent transmission scheduling and dynamically adjust transmission strategies according to network conditions and data volume; The target data adaptation and storage layer is responsible for the final step of data adaptation according to the data structure of the target system, ensuring that the data can be seamlessly integrated into the target system, and storing the migrated and integrated data in the target data storage system; The monitoring and management workstation is responsible for real-time monitoring of the data migration and integration process, including data flow, transmission speed and error handling, so as to track and manage the entire data migration process; Provides a user interface that enables administrators to monitor migration status, manage migration tasks, and generate migration reports; Security Tools: Responsible for implementing security policies including access control, authentication and authorization, protecting data from unauthorized access, and ensuring that data migration and integration processes comply with relevant industry standards.

7. A data migration and integration device based on a distributed cloud platform, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method according to any one of claims 1 to 5 when executing the computer program.

8. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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