End-to-End Automation for Cross-Database DevOps Deployments: CI/CD Pipelines, Schema Drift Detection, and Performance Regression Testing in the Cloud
Enliven Technologies, USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 14(03), 871-889
Publication history:
Received on 09 May 2022; revised on 23 June 2022; accepted on 28 June 2022
Abstract:
Cloud computing systems are increasingly based on heterogeneous databases that combine SQL and NoSQL databases such as Oracle, SQL Server, PostgreSQL, MongoDB, and Cassandra. Although the principles of DevOps have revolutionized the application delivery process, system workflows that involve databases are usually ineffective, performed manually, and extremely vulnerable to failure. The fact that schema updates must be performed manually, cross-engine inconsistencies, and undetected performance regressions create high operational risks, especially in pre-production deployments. Current database DevOps solutions are partially automated but lack full-end solutions that can be used to address schema drift, performance degradation, and deployment reliability of various database systems.
In this study, we propose a completely automated and cloud-based DevOps architecture that combines Continuous Integration and Continuous Delivery (CI/CD) and automated schema drift detection and performance regression testing within a multi-database setup. The design-science research methodology was used to design and test the pipeline, and then controlled experiments of schema variation, migration path, and workload patterns across the chosen database engines were conducted.
The initial results show that the use of automated schema drift detection can greatly minimize inconsistent releases and the late detection of structural anomalies. Automated performance regression testing revealed throughput decreases, latency spikes, and resource inefficiencies with statistically significant confidence levels. Overall, the system minimizes manual intervention and deployment risk, enhances reproducibility, and offers sound performance insights before rolling out production. The findings provide both theoretical and practical value in the emerging discipline of cross-database DevOps automation and useful advice to organizations interested in scalable, cloud-native delivery workflows of databases.
Keywords:
Database DevOps; CI/CD Pipelines; Cross-Database; Automation Schemas Drift Detection Performance Regression; Testing AWS; Cloud SQL and NoSQL; Databases Continuous Delivery Infrastructure as Code Automated Deployment Systems
Full text article in PDF:
Copyright information:
Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0