Python and Scala are two different programming languages which are extremely popular in the fields of data science, data analytics, machine learning and more.
Currently, there is a trending debate about the different benefits of Python vs Scala, and the pros and cons of each language.
We decided to write this article so you can find out some detail about each language, what they offer you and how they differ. It’s really important to know the difference between the tools available so you can choose the one with the right functionality for your next business project.
Otherwise, you risk spending time and money working with a programming language that is not suited to deliver you a return on investment.
First, let’s take a look at some definitions.
What is Python?
Python is a dynamically typed, interpreted programming language, which is one of the most popular choices for modern software development. It supports multiple programming paradigms, including object oriented, functional but also procedural.
It’s modular, so it can be easily integrated with other technologies, and it’s open source, which means it’s free to use and there is a large global community of developers contributing to the Python's codebase and its development.
What is Scala?
Scala is a statically typed language, which means types of objects and variables need to be specified within Scala. It is also a multi-paradigms programming language but offers more advanced functional features like immutability, currying, or lazy evaluation, but it also supports object oriented programming.
It was built for the Java Virtual Machine (JVM) and one of its strengths is that this makes it very easy for it to interact with Java code. Scala’s static typing enables developers to avoid bugs in complex applications more easily, while JVM enable the building of high performance systems with access to extensive libraries.
From those definitions you might have noticed some differences already, but let’s go in-depth to find out about the pros and cons and how Python and Scala compare.
Python vs Scala: The main differences
Here you can read on the top 14 differences between Python and Scala.
Scala, a compiled language, is seen as being approximately 10 times faster than an interpreted Python because the source code is translated to efficient machine representation before the runtime. On the other hand, Python being an interpreted language allows for a faster development process as the developer doesn’t have to wait for the compilation after each change.
As Scala is based on Java Virtual Machine, it benefits from its many performance optimizations introduced over the years, and it is much faster when processing data, so for any projects related to the use of big data or compute-intensive applications, it’s preferable to Python.
Python runs on a dedicated interpreter that is available for multiple platforms, including among others, Windows, macOS and other modern Unix-like systems. Unlike Python, Scala is based on JVM, so its source code is compiled to Java bytecode before being executed by JVM. Therefore Scala is available for all platforms that are supported by JVM, which includes the same platforms as listed for Python.
Scala has several standard libraries and cores, which enable Big Data ecosystem databases to be quickly integrated, and also allows the writing of code with multiple concurrency primitives. In Scala, you can use both internal Java and Scala APIs.
An additional event-based concept is the
Akka library. With Akka, you can do concurrency like in Erlang language - based on the actor model. This offers huge power to implement more reliable systems.
Unlike Scala, Python does not support this kind level of concurrency or multithreading, but it's possible. The concurrency feature means Scala enables better memory management and quicker data processing.
Nevertheless, Python does support heavyweight process forking in which a single thread is active at a time. This means that when a new code is deployed, more processes need to be restarted, increasing the overall memory overhead and time taken for data processing.
Python API has also implemented the Asynchronous I/O ( asyncio) concept well. With this, you can easily build multitasking solutions without the adaptation overhead.
4. Applications in machine learning and data science
Python has multiple libraries for Machine Learning, Natural Language Processing (NLP) and data science tools, whereas Scala doesn’t have any such tools.
For this reason, Python is currently the preferred language among data scientists and those working in Machine learning. It’s easy to learn and implement and offers access to extensive libraries and frameworks.
It has a wide range of very useful libraries for use in machine learning and data science projects, like NumPy, Pandas, Matplotlib, SciPy and more, as well as libraries for complex deep learning projects - Keras, TensorFlow, Pytorch and more.
Scala is the core language used to write the most popular distributed big data processing framework Apache Spark. Big Data processing is becoming inevitable for small to large enterprises, and Scala is very important for data engineering and data science teams. Scala offers more analytics power for the biggest data volumes such as Petabytes, Zettabytes, and more.
The Scala language shares several readable syntax features of popular languages such as Ruby and also has functional features like pattern matching, string comparison advancements, and more, which incorporate functions within class definitions.
Scala language has more features for developing skills in software engineering. Primarily, there are functional programming and Domain Specific Language tools inside. With the Functional Programming paradigm, developers can simplify solutions based on mathematical theories and a more readable codebase.
Domain-Specific Language is an internal Scala feature for building dedicated languages based on Scala for a better understanding of the domain. For example, if you need to write code for Quantum Computing, it's possible to build a special dialect in Scala for this.
Python, meanwhile, has numerous features which have helped to make it a popular software development tool - it’s powerful, fast, easy to learn, has efficient high level data structures and a simple but effective approach to object-oriented programming (OOP). OOP concepts such as interfaces and encapsulation require more work in Python, but are an integral part of the Scala language.
Both Python and Scala are expressive languages, able to offer high levels of functionality. Python could be said to be more user friendly and concise, where Scala boasts more powerful framework, libraries and macros.
Many of Scala’s data frameworks follow similar abstract data types, consistent with its collection of APIs, so developers using Scala just need to learn the standard, basic collections which offer easy acquaintance with other libraries.
Scala is a strict type programming language and the compiler gives suggestions about possible code fails. This means that if you write something incorrectly in the code, the compiler will block you from releasing it - basically a real-time quality check and control tool.
Spark is written in Scala so knowing Scala will let you understand and modify what Spark does internally. Furthermore, the majority of upcoming features will have their first APIs in Scala and Java, whereas the Python APIs will evolve in later versions.
Python is preferred for machine learning, NLP, GraphX, GraphFrames and MLLib as Scala has limited or no tools for these types of projects.
7. Code restoration and safety
Scala is a statically typed language, making it easier to find compile-time errors. Python is a dynamically typed language, so it can be more prone to bugs whenever changes are made to existing or source code - this means that code restoration or refactoring is easier and more efficient in Scala than it is in Python.
It depends on how your codebase is divided in packages. Both languages offer code restoration. In fact, if you do more packages and subpackages with dedicated quality control such as unit tests, integration tests and mutation tests, refactoring code is quite easy.
One important consideration for Scala is that it has strict types and all IDE (code editor) suggestions are from compiler errors, so you can find more bugs before run time. In Python this could be a problem, because if you don’t use type hints and type hints checker, you’re less likely to spot bugs before runtime.
Python and Scala both offer extensive libraries. Python’s large community and open source code means it has an extensive network of libraries and frameworks to work from, while Scala enables the use of most of the JVM libraries.
As we’ve already mentioned, Python has the edge when it comes to machine learning libraries, but Scala also has a huge ecosystem of libraries for building high-performance systems.
9. Developer community
Python’s community is much larger than Scala’s, and hence it offers more in terms of support and the ability to draw on libraries dedicated to different task complexities. Python’s developer community is estimated at over 8 million people, while Scala currently lags behind at around 900,000.
This doesn’t mean, however, that Scala doesn't have a strong developer community. It does - helped by the fact that it is also an open source language, like Python - but Python’s popularity means its community is much bigger.
10. Easiness to learn
At the heart of the Python vs Scala debate is the learning curve of each language. Both of them are functional, object-oriented languages with similar syntax, as well as great developer communities. This means they can both be easy to learn, but Scala is more complex in some cases because of its high level functionalities.
The logic of Python is intuitive and simple, and it has good standard libraries, which means the learning curve isn’t that steep. Scala is preferred for more complex workflows, and that level of complexity requires more work to learn.
Scala offers various integrations with other systems and tools. Scala is easily integrated with Apache Spark which makes it a popular choice for Big Data models. Scala is also compatible with the Apache Spark engine. Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
Spark is written in Scala as it can be quite fast because it's statically typed and it compiles in a known way to the JVM. Python also integrates with Spark, but 80% of Spark is written in Scala. If we’re talking about Big Data, there is a pySpark implementation library in Python, so you can connect to the Apache Spark framework with Python and Java wrapper.
Python is better suited for small-scale projects, whereas Scala can be used for projects of bigger scale. This is because Python doesn’t offer any scalable feature support, whereas Scala provides easy, low latency scalability. The name Scala is actually an acronym for ‘scalable language’, so this scalability is one of its core selling points and a reason why many large enterprises choose to use Scala.
Scalability also depends to some extent on architecture. While Python is great for serverless scalability, Scala requires more memory and a dedicated environment such as Java Virtual Machine. Of course we can do biggest projects with Python language, if we talk about microservice architecture - it's important point of view.
As a dynamically typed programming language, Python’s testing process and methodologies can be quite complex, whereas Scala is a statically typed language, making it easy to run tests through the code. However type hints in Python with e.g. pydantic library offer more declarations about your data structures and processes.
It’s interesting that both Python and Scala are moving in within the testing domain in order to advance as programming languages; both languages have developed libraries for verification unit test by mutation testing concepts, which is a huge step for software engineers and an important advance in code quality.
As we’ve already mentioned, Python’s larger community means it gives access to more support than Scala. However, Scala does offer strong support for static typing, enabling the easy identification and solving of bugs or issues. It also has a fantastic community specifically within the domain of Big Data and Analytics.
Get to know the differences between Python and Scala
So we’ve seen that Python and Scala have a few similarities, such as being open source or used for data processing. These similarities are the reason many people are comparing them right now but as we’ve seen they also have a lot of differences as well, which means each one will be better for certain types of projects or companies.
Scala’s static typing and scalability makes it a better option for larger and more complex projects - bugs can be easily found and fixed and it is very scalable. Python is great for its developer community, support and agility with extensive libraries for the likes of machine learning and data science.
There’s a reason some of the world’s biggest tech companies - Google, Spotify, Facebook and Instagram to name a few - use Python for their projects. It’s free, versatile and powerful.
It’s also no surprise that many large organizations are also using Scala now as well, such as Asana, Groupon, LinkedIn, Reddit and Twitter. It’s great for Big Data analytics, back-end code, scripts, software development, and web design. Software programmers also cite Scala’s seamless integration of object-oriented features and functional languages as a great solution for parallel batch processing, data analysis and ad hoc scripting.
So, whatever type of project you’re undertaking, you need to understand what the tools you use can offer vs the competition. In the Python vs Scala debate, there’s no ‘right answer’ - you just have to choose the one that suits your business needs most. If you’re still unsure, get in touch with us today, we will be happy to help.