Building Resilient and Scalable Data Pipelines with Apache Airflow and AWS
Author(s): Ujjawal Nayak
Publication #: 2508029
Date of Publication: 18.08.2025
Country: United States
Pages: 1-3
Published In: Volume 11 Issue 4 August-2025
DOI: https://doi.org/10.5281/zenodo.17062996
Abstract
Enterprises increasingly depend on data pipelines that remain reliable under failure and elastic under load. Apache Airflow, coupled with Amazon Web Services (AWS), provides a versatile foundation for orchestrating complex Extract–Transform–Load (ETL) and Extract–Load–Transform (ELT) workflows while meeting stringent uptime and performance goals. This article presents a practical blueprint for building resilient and scalable pipelines using Airflow as the control plane and AWS as the execution substrate. We detail architectural choices for ingestion, processing, storage, and observability; discuss high-availability practices such as multi-region replication and automated recovery; and outline scaling and cost-efficiency patterns using managed services. A brief case study illustrates measurable benefits from migrating event-driven jobs to Airflow and applying autoscaling and observability improvements in production [1]–[10].
Keywords: Apache Airflow, AWS, Data Pipelines, Scalability, Fault Tolerance, Workflow Orchestration, Big Data, ETL, Cloud Computing
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