Modeling and Orchestration of Complex ETL Pipelines in Distributed and Cloud-Native Environments

Ramesh Tangudu

doi:10.30574/ijsra.2024.13.1.2104

ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Modeling and Orchestration of Complex ETL Pipelines in Distributed and Cloud-Native Environments

Ramesh Tangudu ^*

Enterprise Architect and Application Development Lead, TX, USA.

Research Article

International Journal of Science and Research Archive, 2024, 13(01), 3637-3646.

Article DOI: 10.30574/ijsra.2024.13.1.2104

DOI url: https://doi.org/10.30574/ijsra.2024.13.1.2104

Publication history

Received on 19 September 2024; revised on 24 October 2024; accepted on 29 October 2024

Abstract

Aim: The aim of this study is to investigate effective modeling and orchestration strategies for complex ETL (Extract, Transform, Load) pipelines in distributed and cloud-native environments. Modern data-driven systems demand scalable, reliable, and maintainable ETL workflows capable of handling heterogeneous data sources and high data velocity.

Method: This work adopts a conceptual and architectural modeling approach, combining workflow abstraction, distributed processing paradigms, and cloud-native orchestration tools. ETL pipelines are modeled using directed acyclic graphs (DAGs), containerization, and microservices-based deployment strategies.

Results: The proposed modeling and orchestration framework improves pipeline modularity, fault isolation, and execution efficiency. Experimental observations demonstrate enhanced scalability, reduced failure recovery time, and better resource utilization compared to monolithic ETL designs.

Conclusion: The study concludes that cloud-native orchestration combined with structured ETL modeling significantly enhances operational robustness and flexibility. These techniques provide a strong foundation for building future-proof data integration platforms in large-scale distributed environments.

Keywords

ETL Pipelines; Distributed Systems; Cloud-Native Architecture; Workflow Orchestration; Data Engineering

Download Article PDF

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2024-2104.pdf

Preview Article PDF

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.

All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.