Notebooks

Where the data engineer meets the data scientist will be at the negotiating table. This is where the science and the art get transformed into a solution that can operate and be maintained for a high-quality delivery. And on that table, now manifested as the analyst’s workbench, the tools and processes to make the data science or analyst magic take place. This way the art and science come together to be implemented in the engineer’s solution. In the center is a notebook that allows the insights to form. Modern data analytics approaches make the manipulation of data in the notebook easy for the data scientist. Data is brought to life with step-by-step scripted recipe transformations. Data manipulations across small and big datasets via Python, Java, Scala, R, C#, and so on make it possible to code the core of an MVP’s features in order to prove the viability of the art that will have to be made production ready. This key point is often a bone of contention...