Python for Apache Spark is quite easy to learn and use. However, this is not the only reason why Pyspark is a better choice than Scala. The Python API for Spark may be slower on the cluster, but in the end, data scientists can do much more with it compared to Scala. Scala's complexity is absent.
If you have enough experience with any statically typed programming language like Java, you can stop worrying about not using Scala at all. There is a growing demand for Scala developers because big data companies value developers who can master a productive and robust programming language for data analysis and processing in Apache Spark. Data scientists often prefer to learn both Scala for Spark and Python for Spark, but Python is often the second-favourite language for Apache Spark, as Scala came first. Scala also provides better performance than Python due to its speed and therefore may be the preferred choice of programming language when it comes to handling large datasets.
Before choosing a language for programming with Apache Spark it is necessary for developers to learn Scala and Python to familiarise themselves with their features. Choosing a programming language for Apache Spark is a subjective matter, as the reasons why a particular data scientist or analyst likes Python or Scala for Apache Spark do not always apply to others. Performance is mediocre when Python programming code is used to make calls to Spark libraries, but if there is a lot of processing involved, the Python code becomes much slower than the equivalent Scala code. Scala and Python languages are equally expressive in the context of Spark, so using Scala or Python can achieve the desired functionality.
From a more personal point of view, I find PySpark easier to learn and read, however for production Scala is a much nicer programming language. A quick look at the salaries offered by Python and Scala skills shows that Scala as a skill offers more salary in the job market than Python. Scala is definitely the better choice for the Spark Streaming role because Python Spark support is not as advanced and mature as Scala. For this reason, many people program in PySpark and when all the code is validated, they move to production in Scala.
Scala offers a lot of advanced programming features, but you don't need to use any of them when writing Spark code. You will master the essential skills of the open source Apache Spark framework and the Scala programming language. Let's explore some important factors to consider before deciding on Scala over Python as the primary programming language for Apache Spark. Refactoring the code of a statically typed language like Scala is much easier and hassle-free than refactoring the code of a dynamic language like Python.