Takuya Ueshin is a software engineer at Databricks, and an Apache Spark committer and a PMC member. His main interests are in Spark SQL internal, a.k.a. Catalyst, and also PySpark. He is one of the major contributors of the Koalas project.
In this talk, we present Koalas, a new open-source project that aims at bridging the gap between the big data and small data for data scientists and at simplifying Apache Spark for people who are already familiar with the pandas library in Python.
Pandas is the standard tool for data science in python, and it is typically the first step to explore and manipulate a data set by data scientists. The problem is that pandas does not scale well to big data. It was designed for small data sets that a single machine could handle.
When data scientists work today with very large data sets, they either have to migrate to PySpark to leverage Spark or downsample their data so that they can use pandas. This presentation will give a deep dive into the conversion between Spark and pandas dataframes.
Through live demonstrations and code samples, you will understand: - how to effectively leverage both pandas and Spark inside the same code base - how to leverage powerful pandas concepts such as lightweight indexing with Spark - technical considerations for unifying the different behaviors of Spark and pandas