Building Resilient and Scalable Data Pipelines with Apache Airflow and AWS
Year: 2025
Type: article
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].
Author: Ujjawal Nayak
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7080133474
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