PySpark vs Scala: What are the differences?
PySpark: The Python API for Spark. It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data; Scala: A pure-bred object-oriented language that runs on the JVM. Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
PySpark can be classified as a tool in the "Data Science Tools" category, while Scala is grouped under "Languages".
Scala is an open source tool with 11.9K GitHub stars and 2.76K GitHub forks. Here's a link to Scala's open source repository on GitHub.
According to the StackShare community, Scala has a broader approval, being mentioned in 557 company stacks & 1895 developers stacks; compared to PySpark, which is listed in 8 company stacks and 6 developer stacks.