Data scientists spend 60 percent of their time cleaning and preprocessing data, transforming this dirty data into crystallized insights. DataFrames, such as pandas, provide exceptional tooling to address data wrangling tasks, yet pandas themselves increasingly lack ease and speed as they scale. Alex Baden and Devin Petersohn explore the challenges and considerations of DataFrame scaling. They explore how the Intel® Distribution of Modin* and OmniSci solution, part of the Intel® oneAI AI Analytics Toolkit, offers an open road to quick, transparent scaling across heterogeneous architectures. They also explain how this solution’s integration with the rest of the Python* ecosystem enables data scientists to focus on extracting value from data rather than provisioning and orchestrating resources.