Scala and Python for Apache Spark

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What is Scala?:

Scala combines object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.

What is Python?:

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.



Both Python and Scala programming languages offer a lot of productivity to programmers. They are useful tools among data scientists. Most learn both languages for Apache Spark. However, majority prefer Scala to Python for Apache Spark due to speed (Like ten times faster than Python). Scala helps handle the complicated and diverse infrastructure of big data systems. Scala does help in identifying time errors. Even though Scala is fast and powerful, there are many complexities with it. Recently Python is gradually taking over Scala.

Why is Python gradually taking over Scala?:

Python API for Spark may be slower on the cluster, but at the end, big data analysts can do a lot more with it as compared to Scala. The interface is simple, comprehensive, and not as complex as Scala. Python comes with several libraries related to machine learning and natural language processing. For example : pandas, numpy, scikit-learn, seaborn etc. while Scala has fewer libraries that makes it much more difficult. It is useful for a data scientist to learn Scala, Python, R, and Java for programming in Spark and choose the preferred language based on the efficiency of the functional solutions to tasks. .

Scala community often turns out to be lot less helpful to programmers compared to Python.

Table bellow shows the overview of their features and how they differ from each-other in satisfying big data analyst/data scientist's needs:

Feature Scala Python
Performance 10 times faster than Python Slower
Learning Curve Scala’s arcane syntax makes it difficult to master. So therefore, It is complex. Python is comparatively easier to learn for java programmers because of its syntax and standard libraries.
Concurrency Supports powerful concurrency through primitives. Python does not support true multithreading.
Type safety Statically typed language Dynamically Typed Language
Ease of Use Verbose language Less verbose and easier to use
Advanced Features Has several existential types, macros and implicit but lacks good visualization and local data transformations Several libraries for Machine Learning and Natural Language Processing

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