Kubernetes (K8s) has emerged as the de facto standard for container orchestration as cloud-native architectures gain widespread adoption. Organizations are increasingly managing large-scale K8s clusters to support data-intensive workloads, such as batch processing and Extract, Transform, Load (ETL) jobs. While K8s offers flexibility and scalability, it also introduces significant operational challenges. Managing K8s clusters requires substantial administrative effort and operational costs, including provisioning, scaling, security, and performance optimization. Achieving high availability while optimizing resource utilization demands continuous monitoring and fine-tuning, leading to increasing expenses and complexity. These challenges drive organizations to explore alternative deployment approaches that retain the benefits of K8s while reducing administrative overhead. Serverless containerization has emerged as a promising solution, integrating the advantages of serverless computing with containerization. This approach automates scaling, manages infrastructure provisioning, and dynamically optimizes resource consumption capabilities that standard K8s deployments often lack. By reducing infrastructure management burdens, serverless containerization allows enterprises to focus on designing and executing workloads rather than maintaining complex deployment environments. This study presents a qualitative analysis of major serverless container platforms offered by public cloud providers, followed by the selection of Azure Container App jobs for implementation. The chosen platform is used to deploy Knowledge Synchronizer (KS) a set of backend ETL tools developed by the Hydraulic Hub team at Bosch Rexroth using Azure Container App (ACA) Jobs, a serverless containerization platform by Microsoft Azure. These applications automate data synchronization between distributed systems and serve as a representative workload for the study. Key platform metrics, including CPU and memory utilization, network throughput, and cold start latency, are collected and insights are drawn from the observed workload behavior to evaluate the suitability of serverless containerization for enterprise-grade, data-intensive operations. The findings aim to inform architectural decisions for similar cloud-native deployments. Keywords: Serverless Containerization, Kubernetes, Extract Transform Load, Cloud