A Unified CI/CD Orchestration Model for Continuous ETL Deployment in Multi-Cloud Environments

Pramod Raja Konda

Abstract


Modern enterprises increasingly rely on complex Extract–Transform–Load (ETL) pipelines to support analytics, reporting, and artificial intelligence workloads across distributed cloud platforms. As organizations adopt multi-cloud strategies to avoid vendor lock-in, optimize cost, and improve resilience, managing continuous deployment of ETL pipelines becomes significantly more challenging. Traditional CI/CD practices, originally designed for application development, struggle to accommodate the operational complexity, dependency management, and data quality requirements of large-scale ETL workflows deployed across heterogeneous cloud environments. This research proposes a unified Continuous Integration and Continuous Deployment (CI/CD) orchestration model specifically designed for continuous ETL deployment in multi-cloud environments. The model introduces a standardized orchestration layer that integrates source control, automated testing, schema validation, environment-aware deployment, and cross-cloud execution management. By treating ETL pipelines as first-class deployable assets, the proposed approach enables consistent versioning, automated rollback, and governance-aware deployment across multiple cloud platforms. The orchestration model incorporates infrastructure abstraction, pipeline dependency management, and data-aware deployment gates to ensure reliability and consistency during continuous delivery. Automated validation mechanisms verify schema compatibility, data transformations, and runtime configurations prior to deployment, reducing failure rates and operational risk. The framework also supports environment-specific execution policies and seamless promotion of ETL pipelines from development to production across cloud boundaries. An enterprise case study demonstrates that the proposed unified CI/CD orchestration model significantly reduces deployment failures, shortens release cycles, and improves operational visibility in multi-cloud ETL environments. The findings highlight the importance of domain-specific CI/CD architectures for data engineering and establish a scalable foundation for continuous, reliable ETL deployment in modern data platforms

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References


Bernstein, P. A., & Rahm, E. (2011). Data integration in the cloud. ACM Data Engineering Bulletin, 34(1), 3–13.

Chen, Y., Lee, K., & Wang, L. (2014). DataOps: A collaborative data management approach. IEEE Computer, 47(10), 85–88.

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.

Doan, A., Halevy, A., & Ives, Z. (2012). Principles of data integration. Morgan Kaufmann.

Hellerstein, J. M., Stonebraker, M., & Hamilton, J. (2007). Architecture of a database system. Foundations and Trends in Databases.

Humble, J., & Farley, D. (2010). Continuous delivery: Reliable software releases through build, test, and deployment automation. Addison-Wesley.

Kimball, R., & Ross, M. (2013). The data warehouse toolkit. Wiley.

Rahm, E., & Do, H. (2000). Data cleaning: Problems and current approaches. IEEE Data Engineering Bulletin.

Redman, T. C. (2013). Data driven: Profiting from your most important business asset. Harvard Business Review Press.

Zhu, Q., & Chen, H. (2016). Semantic-based ETL process design. Expert Systems with Applications, 55, 56–67.


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