ARCHITECTURAL OPTIMIZATION OF CLOUD-NATIVE DATA PROCESSING PIPELINES: A SYSTEMATIC ANALYSIS OF SERVERLESS COMPUTING PARADIGMS

Abstract

This article comprehensively analyzes enterprise-scale serverless data processing compute platforms - AWS EMR Serverless, Google Cloud Dataproc Serverless, and Azure Synapse Serverless - going beyond traditional static resource compute jobs and lightweight serverless computing to address large-scale data analytics needs. The article examines how these specialized platforms enable complex batch processing, near real-time streaming jobs, ETL operations, and distributed machine learning workloads on petabyte-scale datasets while maintaining serverless benefits of dynamic scaling and consumption-based pricing. Through a systematic evaluation of enterprise implementations, this study investigates architectural patterns and operational strategies specific to big data processing, including distributed computation frameworks, data lake integration approaches, and performance optimization techniques for data-intensive workloads. Case studies from organizations demonstrate the practical benefits and challenges of migrating traditional big data workloads to serverless platforms, providing insights into performance improvements, cost optimization, and operational efficiency gains in processing large-scale datasets with flexibility. The findings reveal that serverless big data architectures, when properly implemented, can significantly reduce infrastructure costs and operational complexity while maintaining processing performance and reliability for enterprise-scale analytics workloads. This article contributes to the growing body of knowledge on cloud-native data processing by providing a structured framework for evaluating and implementing serverless big data pipelines and recommendations for addressing common challenges in large-scale data processing environments.