Performance is mediocre when Python programming code is used to make calls to Spark libraries, but if there is a lot of processing involved, Python code becomes much slower than the equivalent Scala code. Yes, it may seem more complex to the Scala novice, but once you fully understand the concepts behind it, Scala code will seem much more simplistic than Java code. The rule of thumb here is that using Scala or Python - developers can write the most concise code and using Java or Scala can achieve the best runtime performance. Many organisations favour the speed and simplicity of Spark, which supports many application programming interfaces (APIs) available from languages such as Java, R, Python and Scala.
The biggest names in the digital economy are investing in S cala programming for big data processing - Kafka created by LinkedIn and Scalding created by Twitter. The design of Scala started in 2001 at the EPFL (École Polytechnique Fédérale de Lausanne) programming methods lab. 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. Its API is designed for data processing and analysis in multiple programming languages such as Java, Python and Scala.
Scala also offers closures, a feature that dynamic languages such as Python and Ruby have adopted from the functional programming paradigm. Other companies such as Apple, The Guardian, Meetup, Verizon, SoundCloud, Airbnb and Duolingo use Scala in certain teams or have made statements that they will move to Scala. If you have enough experience with any statically typed programming language like Java, you can stop worrying about not using Scala at all. One thing you may not know about Scala is that it was originally developed at the Swiss university EPFL in an attempt to apply recent innovations in programming language research to a language that could gain traction in the mainstream, such as Java.
Coupled with the superiority of the functional programming idioms available to take advantage of multi-core CPU architectures, Scala has the right mix of the popular object-oriented paradigm. All these statistical reports show how Scala programming is becoming the choice for Apache Spark to make data analysis faster. Before choosing a language for programming with Apache Spark, it is necessary for developers to learn Scala and Python to become familiar with their features. This combination of features makes it possible to write programs in Scala that are quite concise and elegant.