Data scientists leverage the power of notebooks every day. They can cleanse, explore, and visualize data within notebooks. They can import their favorite packages, such as pandas, and harness the power of machine learning libraries as well. Clearly, a notebook is more than just a tool, it is the cornerstone of today’s data analytics. However, today’s users access open source notebooks independently of the data platform. As a result of this separation, data must be made accessible within notebooks, and this eats up valuable analytics time. In addition, having insights siloed outside of the platform causes inefficiencies in the journey from model to actionable business insight.