Designing Scalable Data Engineering Pipelines for Machine Learning in Cloud-Native Ecosystems

Abstract

Modern machine learning systems depend heavily on robust and scalable data engineering pipelines capable of handling high-volume, high-velocity, and heterogeneous data sources. This research explores architectural patterns and engineering best practices for building cloud-native data pipelines that support machine learning workloads at scale. The study evaluates distributed ingestion frameworks, schema evolution strategies, data validation layers, and orchestration mechanisms using real-world enterprise scenarios. Experimental results demonstrate improvements in pipeline reliability, data freshness, and downstream model performance when modular and event-driven architectures are adopted.