Sarcouncil Journal of Multidisciplinary

Abstract: The transition of data processing architectures from traditional batch-processing systems to cloud-native paradigms constitutes a paradigm shift in enterprise data management approaches. Serverless computing paradigms have become feasible alternatives to traditional infrastructure-based models, with scaling features and event-driven processing patterns alleviating infrastructure management complexities. Event-driven architectures enable near-real-time response to data availability, along with cost-efficient resource usage through pay-per-execution pricing models. Containerized solutions offer enormous advantages through persistent stateful processing features, sophisticated orchestration patterns, and high-level integration patterns enabling complex data transformation workflows. The performance attributes are significantly different across architectural paradigms, with serverless platforms showing enhanced efficiency in variable workload patterns, while containerized solutions provide consistent performance statistics for continuous processing requirements. Cost optimization features differ significantly based on workload patterns, with serverless architectures being cost-effective for ad-hoc processing patterns, and containerized deployments being effective for high-volume sustained activities. Integration features encompass varied connectivity to data sources, sophisticated error handling processes, and comprehensive monitoring frameworks enabling reliable pipeline execution. This article discusses the architectural trade-offs, performance considerations, and operational implications that influence technology selection criteria for cloud-native data processing applications.