Data processing time is so valuable as each minute-spent costs back to users in financial terms. This article is mainly for data scientists and data engineers looking to use the newest enhancements of Apache Spark since, in a noticeably short amount of time, Apache Spark has emerged as the next generation big data processing engine, and is highly being practiced throughout the industry faster than ever.
Spark’s consolidated structure supports both compatible and constructible APIs that are formed to empower high performance by optimizing across the various libraries and functions built together in programs enabling users to build applications beyond existing libraries. It gives the opportunity for users to write their own analytical libraries on top as well.

#machine-learning #data-science #spark #ai #python

Distributed Processing PyArrow-Powered New Pandas UDFs in PySpark 3.0
1.55 GEEK