Almost every organization today is promoting data-driven decision making leveraging advances in data science. According to various surveys, data scientists spend up to 80%of their time cleaning and transforming data. Although data management systems have been carefully optimized for such tasks over several decades, they are seldom leveraged by data scientists who prefer to use libraries such as Pandas, sacrificing performance and scalability in favor of familiarity and ease of use. As a result, data scientists are not able to fully leverage the hardware capabilities of commodity workstations and either end up working on a small sample of their data locally or migrate to more heavyweight frameworks in a cluster environment. In this paper, we present PyFroid, a framework that leverages lightweight relational databases to improve the performance and scalability of Pandas, allowing data scientists to operate on much larger datasets on a commodity workstation. PyFroid has zero learning curve as it maintains all the Pandas APIs and is fully compatible with the tools that data scientists use (e.g., Python notebooks). We experimentally demonstrate that, compared to Pandas, PyFroid is able to analyze up to 20X more data on the same machine, provide comparable or better performance for small datasets as well as near-memory data sizes, and consume much less resources.<\/p>\n","protected":false},"excerpt":{"rendered":"
Almost every organization today is promoting data-driven decision making leveraging advances in data science. According to various surveys, data scientists spend up to 80%of their time cleaning and transforming data. Although data management systems have been carefully optimized for such tasks over several decades, they are seldom leveraged by data scientists who prefer to use 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