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ZENODO
Other literature type . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Project proposal . 2025
License: CC BY
Data sources: Datacite
ZENODO
Project proposal . 2025
License: CC BY
Data sources: Datacite
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Database Optimization Using Containerized Pipelines

descriptionPublicationkeyboard_double_arrow_right Project proposal , Other literature type 03 May 2025 English Publisher:Zenodo
Authors: Deepti Kaushik;

doi: 10.5281/zenodo.15328406 , 10.5281/zenodo.15328407

Database Optimization Using Containerized Pipelines

Abstract

Modern database systems require more than manual tuning to meet the performance demands of dynamic, large-scale applications. This research presents a DevOps-driven solution that integrates containerized CI/CD pipelines for automated MySQL database optimization. By utilizing tools like Docker, Jenkins, Prometheus, and Grafana, the framework enables continuous performance monitoring and proactive tuning. The approach offers a scalable, adaptable foundation for enterprise-grade database management.

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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!
0
Average
Average
Average