The point being, though there is differentiation of code and changes in approach (when compared to Java) can make Scala language a bit more difficult, still the result is much clean and well-structured that is ultimately easier to use and read similar to that of the likes of Python. Python vs. Scala Python Python is a high-level, general-purpose language that supports multiple paradigms, including functional, procedural, and object-oriented programming. Python . They can perform the same in some, but not all, cases. Scala vs Python -- too big to get into, not sure what the specific question is here. I was just curious if you ran your code using Scala Spark if you would see a performance… Performance: Scala Vs. Python Talking about the performance of Python and Scala, Scala is considered to be faster than Python as the Scala programming language makes significant use … In general, Scala is faster, but your mileage may vary. * One of the reasons is that Scala is faster is that it uses static data types, while Python uses dynamic typing. The performance is mediocre when Python programming code is used to make calls to Spark libraries but if there is lot of processing involved than Python code becomes much slower than the Scala equivalent code. Scala programming language is 10 times faster than Python for data analysis and processing due to JVM. you can even go through with Scala vs Python in my next blog. 1) Scala vs Python- Performance . Though you shouldn’t have performance problems in Python, there is a difference. Today in this blog we discuss on, which is most preferable language for spark. It's harder to write large-scale testable software systems in Python, IMHO, but easier to write smaller, easy-to-read and easy-to-run analytic programs. These languages provide great support in order to create efficient projects on emerging technologies. It all depends on the nature of program and the libraries used. Regarding PySpark vs Scala Spark performance. Spark job are commonly written in Scala, python, java and R. Selection of language for the spark job plays a important role, based on the use cases and specific kind of application to be developed - data experts decides to choose which language suits better for programming. Copy link Quote reply Member lintool commented May 2, 2018. Why? Python and Scala are two of the most popular languages used in data science and analytics. Projects. Because with Python, there is an additional overhead of JVM communication. Python is also an important language to support for data science. discussion. In this article, we list down the differences between these two popular languages. Comments. When using a higher level API, the performance difference is less noticeable. Spark performance for Scala vs Python (2) Extension to above answers - Scala proves faster in many ways compare to python but there are some valid reasons why python is becoming more popular that scala, let see few of them — Python for Apache Spark is pretty easy to learn and use. Python continues to be the most popular language in the industry. It is one of the most popular and top-ranking programming languages with an easy learning curve. DataFrames and PySpark 1.0.0 Release of AUT. Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance. 6 comments Labels. It has better data science libraries too. 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