Containerized Jupyter Notebooks: balancing flexibility and performance

As the need for platforms that simplify and streamline analysis workflows in multidisciplinary research continues to grow, tools like Jupyter Notebooks have become essential for integrating data processing and collaborative efforts across diverse fields. These tools, while powerful, face challenges in scalability, flexibility, and real-time collaboration. Addressing these challenges, we introduce an evolution of the NextPyter system, an open-source research collaborative platform, through the implementation of an API and deployment using Kubernetes (K8s). These advancements aim to enhance the system’s scalability, flexibility, and management. To justify these technological choices, we conducted a series of performance tests evaluating system responsiveness, resource utilization, and stability under varying load conditions. The results show a slight performance loss due to containerization compared to bare metal, but analytical tasks executed via Jupyter Notebooks perform comparably in both environments.