Conquering a Panda's weaker self - Fighting laziness with laziness

The Python programming language has become very popular among data scientists because of its easy-to-learn syntax and rich ecosystem of libraries. Especially the Pandas framework is widely used for various data processing and analytics tasks. However, due to its memory management and eager evaluation Pandas does not scale and workstations quickly come to their limits even for moderate data set sizes. With Grizzly, we introduce a framework that produces SQL queries for operations on DataFrames, moving complexity from workstations to database servers. Grizzly allows to not only access data already stored in a database, but also to combine it with external data from files. Furthermore, users can use their own user-defined functions or use Grizzly’s model join feature to easily apply machine learning models to data, both being executed inside the database server. This allows for fast and scalable data analytics operations, even with a small workstation.