In the previous article, we have talked about how FireDucks lazy-execution can take care of the caching for the intermediate results in order to avoid recomputation of an expensive operation. In today’s article, we will focus on the efficient data flow optimization by its JIT compiler. We will first try to understand some best practices when performing large-scale data analysis in pandas and then discuss how those can be automatically taken care by FireDucks lazy execution model.
