Scalable Big Data Processing in Cloud Environments: A Performance Evaluation of Containerization and Orchestration Solutions

Abstract

The ever-increasing volume and complexity of data in contemporary cloud environments have necessitated the development of scalable solutions for efficient big data processing. This research article presents a comprehensive performance evaluation of containerization and orchestration solutions in the context of cloud-based big data processing. Through a rigorous methodology, we examine the impact of containerization and orchestration on scalability, resource utilization, and fault tolerance. Our findings reveal that containerization and orchestration solutions indeed enhance scalability in cloud environments, leading to improved resource utilization and fault tolerance. These technologies offer industry practitioners a strategic advantage in optimizing their big data processing workflows, fostering cost-efficiency, agility, and resource optimization. However, our research underscores the critical importance of careful tool selection and configuration. We observe that the specific choice of containerization and orchestration tools significantly influences performance. As such, industry practitioners are advised to conduct thorough assessments of their unique use cases and objectives, ultimately guiding tool selection and fine-tuning. From a policy perspective, this study emphasizes the need for ongoing investment in research and development to promote innovation in cloud-based big data processing. Policymakers are encouraged to facilitate the dissemination of knowledge regarding containerization and orchestration technologies and to develop regulatory frameworks that ensure the secure and ethical handling of big data in the cloud.