Apache Spark has become a popular and successful way for Python programming to parallelize and scale up their data processing. In many use cases, though, a PySpark job can perform worse than equivalent job written in Scala. It is also costly to push and pull data between the user’s Python environment and the Spark master. In this talk, we’ll examine some of the the data serialization and other interoperability issues, especially with Python libraries like pandas and NumPy, that are impacting PySpark performance and work that is being done to address them. This will relate closely to other work in binary columnar serialization and data exchange tools in development such as Apache Arrow and Feather files.