Jupyter Notebooks as First-Class Citizens in Cloud-Native Data Workflows
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I3P110Keywords:
Jupyter, Notebooks, Cloud-Native, DevOps, CI/CD, Data Workflows, Containerization, Reproducibility, Orchestration, Kubeflow, Machine Learning PipelinesAbstract
Jupyter Notebooks have quickly become must-have tools for engineers, analysts, and data scientists. They provide a flexible, interactive platform that combines code, visualization, and story in one place. However, even though they are widely used for exploration and experimentation, they have traditionally had trouble fitting into production-grade, collaborative, and scalable data processes. This research looks at how Jupyter Notebooks might be reimagined as the vital parts of cloud-native ecosystems, going beyond their limits to become more important parts of these modern data infrastructure. We look at the latest ways of building and running things that make this change possible, such as CI/CD pipelines, Kubernetes orchestration, and seamless connections with these containerized environments. The goal is to make notebooks more reliable, secure, and scalable, turning them from solo scripts into useful, collaborative tools. We look at how tools like Papermill, JupyterHub, and Kubeflow Notebooks make production more ready while still meeting governance and these compliance needs. A case study shows how a corporate data platform may be useful in actual life and how to put it into action. This point of view stresses how cloud-native notebook methods make it easier for teams to work together, speed up model deployment cycles & create a culture of open, reproducible analytics. The report says that Jupyter Notebooks are not just a way to program, but they are also an important part of the modern cloud-based data and ML pipelines.
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