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Modernizing Legacy Hadoop Infrastructure through Cloud-Native Migration on AWS

Praveen

Abstract
As enterprises seek greater agility and scalability, modernizing legacy Hadoop ecosystems into cloud-native architectures has become a central priority for data-driven transformation. This paper presents a structured and repeatable approach for migrating a 5-petabyte, 200-node on-premises Hadoop environment to the Amazon Web Services (AWS) cloud. The proposed framework emphasizes modular design, automation, and open standards to minimize operational disruption and improve cost-to-performance ratios during migration.

The discussion outlines major limitations of traditional Hadoop deployments—such as rigid scalability, high maintenance costs, and limited resource elasticity—and demonstrates how AWS services like Amazon EMR, S3, Glue, and Athena address these challenges. A real-world case study validates the approach, illustrating how organizations can achieve seamless transition, improved performance efficiency, and substantial cost reduction through phased migration.

Critical challenges such as data egress, schema evolution, reproducibility, and vendor dependency are analyzed, with actionable strategies proposed to mitigate them. The study concludes by exploring emerging trends that shape the future of big data modernization, including serverless analytics, cross-cloud orchestration, and metadata-driven governance.

Keywords: Hadoop modernization, cloud-native data architecture, AWS EMR, S3, Glue, Athena, scalable migration, data lake transformation, ETL automation

DOI:  10.55041/ISJEM00064