Operationalizing Batch Workloads in the Cloud with Case Studies
Abstract: The rapid adoption of cloud computing has transformed the landscape of batch processing, offering unprecedented scalability, flexibility, and cost-efficiency. However, simply migrating existing batch workloads to the cloud (the "lift-and-shift" approach) often fails to fully leverage the cloud's potential. This paper explores strategies and best practices for operationalizing batch workloads in the cloud, going beyond mere migration to achieve true cloud-native optimization. We delve into key considerations such as orchestration, data management, monitoring, error handling, security, and cost optimization. Through a comparative analysis of leading cloud platforms (AWS, Azure, and GCP) and real-world use cases, we provide a comprehensive guide for organizations seeking to unlock the full potential of batch processing in the cloud-native era.
Keywords: Cloud-Native, Batch Processing, AWS, Azure, GCP, Orchestration, Data Management, Monitoring, Error Handling, Security, Cost Optimization
How to Cite?: Ramakrishna Manchana, "Operationalizing Batch Workloads in the Cloud with Case Studies", Volume 9 Issue 7, July 2020, International Journal of Science and Research (IJSR), Pages: 2031-2041, https://www.ijsr.net/getabstract.php?paperid=SR24820052154, DOI: https://dx.doi.org/10.21275/SR24820052154