Demystifying cloud-native data engineering: A comprehensive guide to building scalable, resilient data pipelines in modern cloud environment

Gajula, Mohan (2025) Demystifying cloud-native data engineering: A comprehensive guide to building scalable, resilient data pipelines in modern cloud environment. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 2377-2385. ISSN 2582-8266

This comprehensive article explores the evolving landscape of cloud-native data engineering, offering insights for both newcomers and business stakeholders navigating the complexities of modern data infrastructure. The article examines the fundamental components of scalable data pipelines, beginning with the evolving role of data engineers and their critical function in transforming raw information into actionable business intelligence. It delves into the architectural frameworks supporting end-to-end data workflows—from ingestion and storage to transformation and processing—highlighting key technologies such as Apache Spark, Kafka, and leading cloud platforms that enable organizations to manage diverse data types efficiently. The discussion extends to essential practices in data quality management, governance protocols, and observability systems that ensure pipeline reliability and compliance. It concludes by addressing emerging trends in automation, artificial intelligence integration, and real-time analytics capabilities that are reshaping how enterprises across sectors leverage their data assets for strategic advantage.