An integral approach for complete migration from a relational database to MongoDB
Keywords:
Data Migration, Database Transformation, NoSQL, Big Data, Data, ETL, approachAbstract
Today, computing has become an obligation in the lives of individuals and institutions alike. This magical sector uses and develops very rich, important, and sensitive tools and solutions, which make everyone's life easier. Computers with their tools have become indispensable and they have transformed the world into a small town. These tools revolve around data, the type, nature, and size of which continually increases up to zeta bytes of storage capacity, thus creating BigData. Considering that Relational databases present difficulties in the management of BigData, and Knowing that companies want to keep their data accumulated over decades of exercises and studies on the market, and also taking into account the cost impact if they kept both systems in terms of software, technical support and user training; therefore it is legitimate to find a reliable way to migrate their data from the relational system to a NoSQL system, which designed specifically to handle BigData. In this optic, several studies and approaches have been developed, but they present a lack or weaknesses in the treatment of the main components of the database, which we are going to deal with in our new and integrated approach to migration from the relational database system to MongoDB as a NoSQL system. In this article, we will present our contribution by developing a complete concept of our approach, starting with an introduction, which will be followed by a discussion of what other researchers have done in this direction, then we move on to a phase of analysis and modeling to develop the models and meta-models of the two systems: source and destination, also during this section we also present our analysis and modeling methodology, to present our global approach which divides its treatment into three axes, each of which processes a part of the RDBMS with a particular nature of data: data stored in tables, data carried on the structure of the RDBMS and data coming from the semantics of relational databases. The overall architecture of our approach, which we named ''TMSDRDND'', is formed by two layers: ''TSRSNLayer'' which deals with the transformation of the structure and the transfer of semantic data, and the ''MDRSNLayer'' layer, Which takes care of data migration using an ETL to be developed according to a specific concept and architecture and exploiting the results of the first layer.
Published
How to Cite
Issue
Section
Copyright (c) 2023 Abdelhak Erraji, Abderrahim Maizate, Mohamed Ouzzif

This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Similar Articles
- Gabriel James, Anietie Ekong, Etimbuk Abraham, Enobong Oduobuk, Peace Okafor, Analysis of support vector machine and random forest models for predicting the scalability of a broadband network , Journal of the Nigerian Society of Physical Sciences: Volume 6, Issue 3, August 2024
- A. K. Usman, Y. A. Hassan, A. A. Bery, A. S. Akingboye, M. D. Dick, B. M. Ahmed, R. O. Aderoju, Hybrid deep belief network and fuzzy clustering approach for geothermal prospectivity mapping in northeastern Nigeria using magnetic and landsat data , Journal of the Nigerian Society of Physical Sciences: Volume 8, Issue 1, February 2026
- Nour Hamad Abu Afouna, Majid Khan Majahar Ali, Optimizing precision farming: enhancing machine learning efficiency with robust regression techniques in high-dimensional data , Journal of the Nigerian Society of Physical Sciences: Volume 7, Issue 1, February 2025
- Shehu Magawata Shagari, Danlami Gabi, Nasiru Muhammad Dankolo, Noah Ndakotsu Gana, Countermeasure to Structured Query Language Injection Attack for Web Applications using Hybrid Logistic Regression Technique , Journal of the Nigerian Society of Physical Sciences: Volume 4, Issue 4, November 2022
- Chinedu L. Udeze, Idongesit E. Eteng, Ayei E. Ibor, Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection , Journal of the Nigerian Society of Physical Sciences: Volume 4, Issue 3, August 2022
- S. A. Osikoya, E. O. Adeyefa, Jensen-based New Cryptographic Scheme , Journal of the Nigerian Society of Physical Sciences: Volume 4, Issue 1, February 2022
- Majid Khan Bin Majahar Ali, Shahida Shahnawaz, An inverse physics-informed neural network (I-PINN) framework for parameter estimation in mixed convection and melting effects , Journal of the Nigerian Society of Physical Sciences: Volume 8, Issue 2, May 2026 (In Progress)
- O. J. Olusesi, K. J. Oyewumi, W. A. Yahya, The statistical ensemble of q-deformed hyperbolic modified P\"oschl-Teller potential for certain diatomic molecules through Euler-Maclaurin approach , Journal of the Nigerian Society of Physical Sciences: Volume 7, Issue 2, May 2025
- Ibrahim Adamu Mohammed, Majid Khan Majahar Ali, Sani Rabiu, Raja Aqib Shamim, Shahida Shahnawaz, Development and validation of hybrid drying kinetics models with finite element method integration for black paper in a v-groove solar dryer , Journal of the Nigerian Society of Physical Sciences: Volume 7, Issue 4, November 2025
- A. E. Ibor, E. B. Edim, A. A. Ojugo, Secure Health Information System with Blockchain Technology , Journal of the Nigerian Society of Physical Sciences: Volume 5, Issue 2, May 2023
You may also start an advanced similarity search for this article.

