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Big Data experts have already realized the importance of Spark and Python over Standard JVMs yet there is a common debate on the topic “Which one to choose for big data projects – Scala or Python”. The difference between two may be given based on performance, learning curve, Concurrency, Type Safety, Usability and their advanced features.
The final decision may vary for different data experts as per their convenient level or application type. This is completely the responsibility of Data experts to decide on the best programming language for Apache Spark projects based on functional solutions and efficiency of language.
This is easy to learn both the languages either it is Scala or Python. It allows developers to get productive faster as compared to Java. Scala is often given preference for Apache Spark as compared to Python. The reasons may be different for different data experts. Here, we will give you a quick tour for both of the languages to understand them deeply and choose the best one based on your project requirements. 
Differentiating Scala and Python based on Performance
Scala is ten times faster than Python because of the presence of Java Virtual Machine while Python is slower in terms of performance for data analysis and effective data processing. Python first calls to Spark libraries that involves voluminous code processing and performance goes slower automatically.
At the same time, Scala is good when the number of cores is limited. If they increase in the count, then Scala also start behaving strangely and not liked by the professionals. Here, the question comes performance should be decided based on cores or data processing. Obviously, data processing should be taken as a major deciding factor for performance and there is no doubt that Scala delivers better performance than python for big data Apache Spark projects.
Differentiating Scala and Python based on the Learning Curve
Read: Difference Between Apache Hadoop and Spark Framework
The syntax for Scala is a little bit tricky while Python is easy to learn due to simple syntax and standard libraries.Data professionals have to be extremely cautious while working with Scala. The syntax errors are quite common that can make you crazy sometimes. The libraries are hard to define and they are difficult to be understood by beginners or new programmers.
For a professional developer, not only syntax, but code readability is also taken utmost requirement. There are only few Scala developers that are able to understand this tough programming for big data projects.
At the same time, Python is easy to learn due to simpler syntax and availability of standard libraries, but it cannot be taken as an ideal choice for highly scalable systems like Twitter or SoundCloud. The above discussion concludes that learning a tough language like Scala not only increases developer efficiency, but optimized overall programming functionality too.
Differentiating Scala and Python based on Concurrency
Based on the complexity of big data systems, there is quick need of programming language that can integrate various database programs or services together. Scala enjoys high preference here offering multiple standard libraries and core that helps in quick integration of databases in the big data ecosystem.
With Scala, developers can write more efficient, maintainable, and readable code with multiple concurrency primitives. At the same time, Python does not support concurrency and multithreading well.If you are using Python for big data projects, there is only one CPU active in the python process during that particular time interval.
Read: Hadoop Hive Modules & Data Type with Examples
In case, you are interested in deploying new code to the system, then there is an emergency need that multiple processes should be initiated for effective memory management and data processing. Python fails here when it comes to multi-threading and concurrency while Scala has been proved more efficient and easy language to handle these workloads.
Differentiating Scala and Python based on Type Safety
When developing code for Apache Spark projects, it needs to be continuously re-factored by the developers. Scala is a statically-typed language providesan interface to catch compile-time errors. Refactoring code in Scala is hassle-free and easierexperience than a dynamically-typed language likesPython.
Python language is highly prone to bugs every time you make changes to the existing code. This is always better to use Scala for big data projects wherever scalable code is the primary requirement. Python can be used for small-scale projects, but it does not provide the scalable, feature that may affect productivity at the end.
Differentiating Scala and Python based on Usability
When it comes to usability, both Scala and Python are equally expressive and you may achieve desired functionality as required for big data projects. Python is taken more user-friendly language than Scala and it is less verbose too, that makes it easy for the developers to write code in Python for Apache Spark projects. Usability is considered as a subjective factor because it depends on the personal choice of programmer which programming language he likes the most.
Read: Hadoop HDFS Commands Cheat Sheet
Differentiating Scala and Python based on Advanced Features
Scala has various existential types, implicit, and macros. The syntax with advanced features may be little hard as compared to usual functions. If we talk about the professionals then Scala is always more powerful in terms of framework, libraries, implicit, macros etc.
At the same time, Python is taken primary choice for NLP (Natural Language Processing) while Scala does not have that many tools to work machine learning and NLP. The discussion clearly concludes that it completely depends on the nature of the project and it's processing requirement which programming language you prefer the most. For NLP and machine learning, Python is the best choice while stream, streaming, implicit, macros go well with Scala programming language.
Final words: Scala vs. Python for Big data Apache Spark projects
We would like to hear your opinion on which language you have been preferred for Apache Spark projects and the related benefits and downfalls. Your opinion is highly worth for us that would not only help other professionals in the same world but organizations too in deciding on the best programming language.
Read: An Introduction to the Architecture & Components of Hadoop Ecosystem
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