Advanced SQL Techniques for Efficient Data Migration: Strategies for Seamless Integration Across Heterogeneous Systems |
||
|
|
|
© 2024 by IJCTT Journal | ||
Volume-72 Issue-12 |
||
Year of Publication : 2024 | ||
Authors : Sukhdevsinh Dhummad, Tejaskumar Patel | ||
DOI : 10.14445/22312803/IJCTT-V72I12P105 |
How to Cite?
Sukhdevsinh Dhummad, Tejaskumar Patel, "Advanced SQL Techniques for Efficient Data Migration: Strategies for Seamless Integration Across Heterogeneous Systems," International Journal of Computer Trends and Technology, vol. 72, no. 12, pp. 38-50, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I12P105
Abstract
Data migration is essential in modern data management systems. An effective data migration strategy should enable seamless integrations across diverse database systems. This paper introduces advanced SQL techniques for migrating data between heterogeneous systems, such as MySQL and PostgreSQL, ensuring data integrity and minimizing inconsistencies. The key concept is to develop strategies for data transformation, parallel query execution, and batch processing so that an automated framework is developed to reduce manual intervention. The proposed approach achieves impressive performance metrics, with precision at 92%, recall at 90%, and accuracy at 95%, showcasing its effectiveness in detecting positive migrations and minimizing errors. By combining optimization techniques with validation mechanisms, this study offers a robust, scalable solution for efficient and reliable data migration, emphasizing the importance of metric-driven evaluations in achieving seamless system integration.
Keywords
Data migration, SQL techniques, Heterogeneous systems, Accuracy, Precision, Recall, and System integration.
Reference
[1] Mourade Azrour et al., “Internet of Things Security: Challenges and Key Issues,” Security and Communication Networks, vol. 2021, no. 1, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Priyank Sunhare, Rameez R. Chowdhary, and Manju K. Chattopadhyay, “Internet of Things and Data Mining: An Application Oriented Survey,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 3569-3590, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Reeba Zahid et al., “Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective,” Systems, vol. 11, no. 8, pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Rodrigo Laigner et al., “Data Management in Microservices: State of the Practice, Challenges, and Research Directions,” Proceedings of the VLDB Endowment, vol. 14, no. 13, pp. 3348-336, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Bernal John Nicolas, Rodriguez Johanna Patricia, and Portella Jorge, “DBMS and Oracle Datamining,” Preprints, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Harshith Desamsetti, “Relational Database Management Systems in Business and Organization Strategies,” Global Disclosure of Economics and Business, vol. 9, no. 2, pp. 151-162, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Maninti Venkateswarlu, and T.G. Vasista, “Extraction, Transformation and Loading Process in the Cloud Computing Scenario,” International Journal of Engineering Applied Sciences and Technology, vol. 8, no. 1, pp. 232-236, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Bilal Khan et al., “An Overview of ETL Techniques, Tools, Processes and Evaluations in Data Warehousing,” Journal on Big Data, vol. 6, no. 1, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Aiswarya Raj Munappy et al., “Data Management for Production Quality Deep Learning Models: Challenges and Solutions,” Journal of Systems and Software, vol. 191, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Suman Shekhar, “An In-Depth Analysis of Intelligent Data Migration Strategies from Oracle Relational Databases to Hadoop Ecosystems: Opportunities and Challenges,” International Journal of Applied Machine Learning and Computational Intelligence, vol. 10, no. 2, pp. 1-24, 2020.
[Publisher Link]
[11] Parkash Tambare et al., “Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review,” Sensors, vol. 22, no. 1, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Aravind Ayyagari, Pandi Kirupa Gopalakrishna Pandian, and Punit Goel,” Efficient Data Migration Strategies in Sharded Databases,” Journal of Quantum Science and Technology, vol. 1, no. 2, pp. 72-87, 2024.
[CrossRef] [Publisher Link]
[13] Norwini Zaidi et al., “A Review on Data Transformation Approaches for Data Migration Processes from Relational Database to NoSQL Database,” International Journal of Engineering & Technology, vol. 7, no. 4, pp. 3335-3339, 2018.
[Publisher Link]
[14] Alina Sirbu et al., “Human Migration: The Big Data Perspective,” International Journal of Data Science and Analytics, vol. 11, pp. 341 360, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Txomin Hermosilla et al., “Land Cover Classification in an Era of Big and Open Data: Optimizing Localized Implementation and Training Data Selection to Improve Mapping Outcomes,” Remote Sensing of Environment, vol. 268, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Saydulu Kolasani, “Innovations in Digital, Enterprise, Cloud, Data Transformation, and Organizational Change Management Using Agile, Lean, and Data-Driven Methodologies,” International Journal of Machine Learning and Artificial Intelligence, vol. 4, no. 4, pp. 1-18, 2023.
[Google Scholar] [Publisher Link]
[17] Maciej Besta et al., “Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries,” ACM Computing Surveys, vol. 56, no. 2, pp. 1-40, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Rabiah Abdul Kadir, Ely Salwana Mat Surin, and Mahidur R. Sarker, “A Systematic Review of Automated Classification for Simple and Complex Query SQL on NoSQL Database,” Computer Systems Science & Engineering, vol. 48, no. 6, pp. 1405-1435, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Chandrashekar Althati et al., “Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance,” Australian Journal of Machine Learning Research & Applications, vol. 1, no. 2, pp. 38-78, 2021.
[Google Scholar] [Publisher Link]
[20] Harsh Yadav, “Structuring SQL/ NoSQL Databases for IoT data,” International Journal of Machine Learning and Artificial Intelligence, vol. 5, no. 5, pp. 1-12, 2024.
[Google Scholar] [Publisher Link]
[21] Mehmet Kaya, and Elif Yildirim, “Strategic Optimization of High-Volume Data Management: Advanced Techniques for Enhancing Scalability, Efficiency, and Reliability in Large-Scale Distributed Systems,” Journal of Intelligent Connectivity and Emerging Technologies, vol. 9, no. 9, pp. 16-44, 2024.
[Publisher Link]
[22] Harsh Shah, “Optimizing Software Validation Efficiency and Scalability through Mass Parallel Testing Techniques in Complex Development Environments,” International Journal of Intelligent Automation and Computing, vol. 7, no. 5, pp. 90-123, 2024.
[Publisher Link]
[23] Leonardo Guerreiro Azevedo et al., “HKPoly: A Polystore Architecture to Support Data Linkage and Queries on Distributed and Heterogeneous Data,” Proceedings of the 20th Brazilian Symposium on Information Systems, Juiz de Fora, Brazil, no. 50, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Luisa Pena, “Holistic Approaches to Strategically Integrating SQL and NoSQL Solutions in Hybrid Architectures for Optimized Performance and Versatile Data Handling,” Journal of Artificial Intelligence and Machine Learning in Management, vol. 7, no. 1, pp. 93-115, 2023.
[Publisher Link]
[25] Paniti Netinant et al., “Enhancing Data Management Strategies with a Hybrid Layering Framework in Assessing Data Validation and High Availability Sustainability,” Sustainability, vol. 15, no. 20, pp. 1-28, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Tahir Ramzan, and Greyson Alwin, “Comparative Study of SQL vs. NoSQL for High-Performance E-commerce Databases,” pp. 1-18, 2023.
[Google Scholar]
[27] Norwini Zaidi et al., “An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases,” Computer Systems Science & Engineering, vol. 43, no. 3, pp. 1175-1188, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Haralampos Gavriilidis et al., “Towards a Modular Data Management System Framework,” 1st International Workshop on Composable Data Management Systems, Sydney, Australia, pp. 1-6, 2022.
[Google Scholar] [Publisher Link]
[29] Yongjie Zhu, and Youcheng Li, “A Data Sharing and Integration Technology for Heterogeneous Databases,” International Journal of Circuits, Systems and Signal Processing, vol. 16, no. 2, pp. 232-238, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Aicha Aggoune, and Mohamed Sofiane Namoune, “Metadata-driven Data Migration from Object-relational Database to NoSQL Document-Oriented Database,” Computer Science, vol. 23, no. 4, pp. 495-519, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Patrick Giesser et al., “Implementing Efficient and Scalable In-Database Linear Regression in SQL,” IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, pp. 5125-5132, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] G. Madhukar Rao et al., “A Secure and Efficient Data Migration Over Cloud Computing,” International Conference on Applied Scientific Computational Intelligence using Data Science (ASCI 2020), Jaipur, India, vol. 1099, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Hafiz Abdullah, and Rafidah Binti Musa, “Efficient Data Transition Techniques in Complex Systems,” Sage Science Review of Educational Technology, vol. 3, no. 1, pp. 49-72, 2020.
[Publisher Link]
[34] Hira Lal Bhandari, and Roshan Chitrakar, “Comparison of Data Migration Techniques from SQL Database to NoSQL Database,” Journal of Computer Engineering & Information Technology, vol. 9, no. 6, pp. 1-10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Krishna Kishor Tirupati et al., “Improving Database Performance with SQL Server Optimization Techniques,” Modern Dynamics: Mathematical Progressions, vol. 1, no. 2, pp. 450-494, 2024.
[CrossRef] [Publisher Link]