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Is it Useful to Learn Scala or Python for Big Data?

Scala Vs Python

Both Scala and Python are good programming languages for Big Data. However, this is a comparison and one has to be a winner among the two contestants. So, let’s look into each programming language and its benefits. At H2K Infosys, we offer a  40-hour Data Science training with Python, to tap the increasing demand for data science professionals which is especially on a rise, of late.

What is Scala in Big Data context?

Scala is another general-purpose multi-paradigm programming language that is used to build the popular Spark framework. It supports functional programming, OOP, and can be used for structured programming as well. Released in 2004, it is 10x faster than Python. However, Scala is not one of the languages, which is widely used like Python or Java. One can learn Scala, however, the programmer will need support from Java or Python all the same.  

What is Python, and its uses?

Python is a widely used language by top companies in the world. A majority of Data scientists use Python in the Big Data and Data Science technologies. It’s features like open-source, easy to learn, and availability of numerous libraries make it one of the top preferred languages by the companies.

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Scala Vs Python

The world of Data Science and Big Data are divided between the usage of Scala and Python for performing data analysis. Let’s see the differences between the two popular languages and the winner in each.

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Scala is 10x faster than Python. As Scala uses JVM, and statically-typed language, the performance boost in Scala cannot be ignored. What’s more, Spark is designed to work on top of Hadoop’s file system, HDFS. And Spark is developed in Scala, hence, writing Hadoop applications in Scala is way easier.

Python is a dynamically-typed language, reduces the speed.

Scala is the winner in performance.

Learning curve:

Scala has a steep learning curve than Python. By this, we mean that Scala is complex to learn.

Python with its numerous libraries is more preferred, not to mention its massive communities that offer support to developers.

Python is the winner of the learning curve.


While Scala supports multi-threading through better memory management and data processing, Python is not so viable for concurrency. Python can have only one thread running at a time. In the case of Python, when a new code is initiated, the new process has to be started and thereby increasing memory usage.

Scala is the winner where concurrency/multi-threading is concerned.


While both are expressive, Python is more user-friendly and easy to comprehend. Scala is complex and powerful be it with regards to framework, libraries, or macros, etc. However, when it comes to Natural Language Processing (NLP), GraphX, GraphFrames, MLlib, or Machine Learning, Python is preferred over Scala due to the existence of a larger number of ML libraries in Python. Python’s visualization even on PySpark is unbeatable and Scala cannot compete with that.

Python is a close winner over Scala is expressiveness.

Code Restoration and Safety:

Scala is a statically-typed language and helps in finding compile-time errors. Any changes in Python affects the entire program as it is difficult to catch bugs in Python as compared to Scala.

Code Restoration is easier with Scala.

Verdict: Python though slower is easy to use. Scala is a language of choice where Spark is concerned. And Spark is built to work on top of the Hadoop ecosystem. Hence Scala is better for Big Data whereas Data Science with Python is a preferred combination.

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