Citation:

Dung Le, 2025. "Building Fault Tolerant Infrastructure Deployment Pipelines Using Terraform and Airflow", ESP Journal of Engineering & Technology Advancements  5(4): 104-109.

Abstract:

The rapid transformation of cloud computing and distributed systems has created the need to devise a fault-tolerant system for the deployment of pipeline infrastructure that can offer high availability and resiliency in operation. So far, automated customization of the processes of delivery, auditing, and recovery of complex infrastructure settings has become a widespread practice involving tools like Terraform and Apache Airflow.This paper presents definitions of modern methods and techniques for establishing fault-tolerant infrastructure through the application of such tools. It discusses the principles of modular infrastructure design, multi-zone resiliency, multi-cloud solutions, and serverless orchestration, all achieved through observable and declarative workflows. These principles relate to scalability, automated recovery, and real-life fault-handling mechanisms that enhance system resilience.The usage of Terraform and Airflow is analyzed in terms of existing research, modes of application, and their ability to integrate with other products in creating reliable, versatile, and low-maintenance application pipelines.

References:

[1] Kavas, E. (2023). Architecting AWS with Terraform: Design resilient and secure Cloud Infrastructures with Terraform on Amazon Web Services. Packt Publishing Ltd.

[2] Wang, R. (2022). Infrastructure as Code, Patterns and Practices: With Examples in Python and Terraform. Simon and Schuster.

[3] Kyadasu, R. (2024). Exploring Infrastructure as Code Using Terraform in Multi-Cloud Deployments. Available at SSRN 5075647.

[4] Orozco-GómezSerrano, A. (2020). Adaptive Big Data Pipeline.

[5] Datla, L. S. (2022). Fail-Proof by Design: Building Always-On Insurance Infrastructure with Multi-Zone Redundancy and Self-Healing Pipelines. International Journal of Emerging Trends in Computer Science and Information Technology, 3(2), 53-64.

[6] Shchmelinin, D., Kazakin, A., & Marchenko, A. (2025). Orchestrating Data Center Bring-Up: Efficient Strategies for Scalable Infrastructure Deployment.

[7] Demir, M. A. (2020). A Complete, Automated and Scalable Framework for Science and Engineering (Doctoral dissertation, The University of Texas at San Antonio).

[8] Miñón, R., Diaz-de-Arcaya, J., Torre-Bastida, A. I., & Hartlieb, P. (2022). Pangea: an MLOps tool for automatically generating infrastructure and deploying analytic pipelines in edge, fog and cloud layers. Sensors, 22(12), 4425.

[9] Polisetty, S. M. (2024). CLOUD-NATIVE LAKEHOUSES: MULTI-CLOUD STRATEGIES FOR BUSINESS INTELLIGENCE AND DATA ANALYTICS. Technology (IJRCAIT), 7(1).

[10] Ramesh, G., Pai, T. V., Birau, R., Poojary, K. K., Shingad, A. R., Sowjanya, N., ... & Raj, K. K. (2025). A comprehensive review on scaling Machine Learning workflows using Cloud Technologies and DevOps. IEEE Access.

[11] Zahra, R., Rezaeenour, J., Katanforoush, A., & Bidgoly, A. J. (2025). Resilience Engineering in Serverless ETL Systems: Designing for Fault-Tolerance and Workflow Continuity.

Keywords:

Fault Tolerance, Infrastructure as Code, Terraform, Airflow.