Kubernetes for Data Engineering: Orchestrating Reliable ETL Pipelines in Production - supriya gandhari (The American Journal of Engineering and Technology, 2025)
In the current data driven world, organizations are handling larger and more complex datasets to facilitate decision-making, personalization, and real-time insights. This process is centralized with Extract, Transform, Load (ETL) pipelines, which are essential for gathering data from various sources and preparing it for analysis. Although traditional methods of ETL orchestration typically constructed with monolithic schedulers or Cron-based scripts have functioned well historically, they often struggle to meet contemporary demands like dynamic scaling, high availability, cloud-native deployment, and clear observability. Kubernetes, which was initially designed to manage stateless microservices, has now evolved into a flexible platform capable of handling complex, stateful workloads, including data pipelines. Its capability to be declarative, fault tolerant, and a rich ecosystem of native components such as Jobs, CronJobs, StatefulSets, and ConfigMaps can be a compelling approach for orchestrating ETL pipelines that are both scalable and easy to maintain. By utilizing Kubernetes, data teams can containerize each stage of their pipeline, isolate resource management, and enhance operational clarity which results in reduction in pipeline execution times of up to 40% and infrastructure cost savings between 25% and 35% through autoscaling and optimization of spot instances. This paper investigates the effective application of Kubernetes in data engineering for orchestrating production-level ETL workflows. We go deep into using fundamental Kubernetes constructs for scheduling and fault recovery and examine how they integrate with orchestration frameworks such as Apache Airflow, Argo Workflows, and Dagster. Through a detailed review of academic research, industry case studies, and practical design patterns, we evaluate the advantages and disadvantages of Kubernetes in real-world data processing scenarios. We also discuss ongoing issues such as the operational burden, challenges in ensuring data quality, and the steep learning curve linked to adopting Kubernetes. Despite these issues, our results indicate that Kubernetes provides a strong and future-ready framework for developing modular, reliable, and cloud-portable data pipelines, marking it as a crucial component in the advancement of modern data engineering infrastructure.