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Big Data and Cognitive Computing
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Big Data and Cognitive Computing
Article . 2023
Data sources: DOAJ
DBLP
Article . 2023
Data sources: DBLP
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Implementing a Synchronization Method between a Relational and a Non-Relational Database

Authors: Cornelia Györödi; Tudor Turtureanu; Robert S. Györödi; Doina Zmaranda;

Implementing a Synchronization Method between a Relational and a Non-Relational Database

Abstract

The accelerating pace of application development requires more frequent database switching, as technological advancements demand agile adaptation. The increase in the volume of data and at the same time, the number of transactions has determined that some applications migrate from one database to another, especially from a relational database to a non-relational (NoSQL) alternative. In this transition phase, the coexistence of both databases becomes necessary. In addition, certain users choose to keep both databases permanently updated to exploit the individual strengths of each database in order to streamline operations. Existing solutions mainly focus on replication, failing to adequately address the management of synchronization between a relational and a non-relational (NoSQL) database. This paper proposes a practical IT approach to this problem and tests the feasibility of the proposed solution by developing an application that maintains the synchronization between a MySQL database as a relational database and MongoDB as a non-relational database. The performance and capabilities of the solution are analyzed to ensure data consistency and correctness. In addition, problems that arose during the development of the application are highlighted and solutions are proposed to solve them.

Related Organizations
Keywords

Technology, MongoDB, T, database replication, NoSQL, MySQL, database synchronization

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Top 10%
Top 10%
gold