Scala vs Python Comparison

Scala vs Python is one of the trending debates in the developer community. With the growing demand for tools and programming languages for data science, machine learning, and data analytics- developer’s often get confused among- Scala and Python. 

Each language has its own pros and cons, making a suitable choice for different scenarios. So, to help developers find a perfect solution for their next project, we have created this in-depth comparison blog, highlighting similarities and differences between Scala and Python.

Let’s start with a quick introduction to Python and Scala.

What is Python?

• Dynamically typed
• Interpreted programming language
• Object-oriented, and procedural programming approach
• Modular approach 

All these make Python the most versatile language of all, with 51% of developers preferring it for modern-age app development. With its integration with other languages, it can be used to develop every type of application from simple to AI complex with great ease. 

Top brands like Instagram, Spotify, YouTube, Amazon, and others all rely on Python to do the most complex computations. With a wide range of libraries, modules, and app frameworks, Python makes it easier to integrate the latest technologies like Blockchain, AI/ML, DevOps, and others.

Where Python works the best?

• Automation & Scripting
• Data analysis
• Machine learning
• Prototyping and software testing
• Web development
• Other tasks.

What is Scala?

• Scala stands for “Scalable Language”. 
• General-purpose, object-oriented language, with a touch of functional programming attribute
• Quick-and-dirty scripting language
• Built to use Java Virtual Machine (JVM)- interchange Java code, build high-performance apps, using Java’s vast ecosystem of libraries
• Statically typed language- allowing developers to avoid bugs and create cleaner code

Top brands like Netflix, Twitter, Sony Pictures Imageworks, LinkedIn, Xerox, and Siemens use Scala for flexibility and streamlined operations. Not only this, One of the most popular machine learning and data analytics tools, Apache Spark, uses Scala. 

Scala could be the best option for performance-focused applications.

From a bird-eye view, we can witness some similarities and differences between Scala and Python. Before we dive into the differences, let’s understand their similarities.

Similarities between Scala and Python

Here are some similarities or similar concepts that both language follows.

• Both languages emphasize readability and reduce boilerplate code.
• Both support object-oriented and functional programming approaches.
• Both languages come with extensive standard libraries that provide a broad set of tools for different tasks.
• Both offer a built-in REPL (interactive shell) to test code interactively.
• Both languages provide automatic garbage collection to manage memory, freeing developers from manually handling memory management.
• Both can handle concurrent programming.
• Both languages are cross-platform supporting operating systems like Linux, macOS, and Windows.
• Good third-party library support.

So, where do they differ?

Let’s understand.

**** Despite these similarities, the key distinction lies in the target environments—Python is often favored for its ease of use and rapid development, especially in areas like web development and data science, while Scala is preferred for systems requiring high-performance, scalability, and functional programming paradigms, particularly in enterprise and big data contexts.****

Also Read- Mastering Python App Development: Top Frameworks & Tools 2024

Python vs Scala Comparing Modern Languages

Despite these similarities, the key distinction lies in the target environments. Understand each difference in detail.

1. Scala vs Python Performance

Scala is a compiled language, which makes it quite faster than Python, an interpreted language. It is because Scala code is converted into machine code before execution, and Python code is executed line by line during runtime, making Scala faster.

However, Python being an interpreted language is faster since as you do not have to wait for the code to be compiled after each change.

But Scala runs on the Java Virtual Machine (JVM), making it faster while handling large datasets. So, if you want to work with data-heavy projects, Scala is your choice.

2. Platform 

Python code runs on an interpreter that works on many platforms- Windows, macOS, and other Unix-like systems. While, Scala is based on the Java Virtual Machine (JVM), so its code is compiled into Java bytecode before being run. As a result, Scala can run on any platform that supports the JVM, which includes the same platforms as Python.

To run Python programs, you need the Python interpreter. For Scala, you need the JVM (or sometimes JavaScript or LLVM, depending on the environment). While both languages require different tools to run, both the Python interpreter and the JVM are available on most popular platforms.

3. Concurrency

Concurrency means running multiple tasks at the same time.

In Scala, concurrency is handled efficiently using the Java Virtual Machine (JVM). Scala has powerful tools like 

• Akka(a framework for building concurrent applications) and 
• Futures (which allow tasks to run in parallel), 

These tools make Scala great for handling complex, high-performance applications, big data processing, or real-time systems.

Python, concurrency is a bit more limited. It provides tools like 

• Threading, 
• Multiprocessing, and 
• asyncio to run tasks concurrently. 

However, it has a limited concurrency due to Global Interpreter Lock (GIL). It makes it harder to run multiple threads at the same time on multi-core processors. This means that while Python shows concurrency, but not as efficient as Scala for certain performance-heavy tasks.

4.  Scala vs Python for Machine Learning 

Python offers several libraries, making it a perfect choice for developing machine-learning models and simplifying complex tasks and computations. Some popular Python libraries for ML are- TensorFlow, Scikit-learn, and Keras. Apart from this libraries like Pandas and NumPy help prepare data for ML models.

Scala is used for machine learning, too, especially with Apache Spark for big data tasks. However, While Scala can handle large datasets and high performance, is not as suitable as Python for machine learning, due to a lack of suitable libraries.

5. Scala vs Python syntax

Python has simple and easy-to-read syntax, making it beginner-friendly. It uses indentation to define code blocks, resulting in structured code that is easy to maintain and debug. You do not need any extra symbols or punctuation. For example

def greet():

    print("Hello, World!")

While Scala has a more complex syntax. It combines object-oriented and functional programming features, making it difficult for beginners to learn. Scala uses curly braces {} to define code blocks and has a more compact way of writing code compared to Java. for example-

def greet() = {

  println("Hello, World!")

}

6. Extensive Features 

Features of Python

• Easy to learn with simple, readable syntax.
• Uses dynamic typing (no need to declare variable types).
• Has a vast collection of libraries for web development, data science, and machine learning.
• An interpreted language, meaning code is executed line by line for easier debugging.
• Suitable for scripting, automation, and quick development.
• Strong, active community with plenty of resources for support.

Features of Scala

• Supports both object-oriented and functional programming.
• Uses static typing, which reduces runtime errors by defining types at compile time.
• Runs on the Java Virtual Machine (JVM), making it compatible with Java and its libraries.
• Compiled language, is generally faster than interpreted languages like Python.
• Strong support for concurrency, making it ideal for parallel and distributed computing.
• Encourages the use of immutable data structures for improved reliability.
• Easily integrates with Java code and tools.

7. Extensive set of Libraries

Python and Scala both offer an extensive set of libraries. Python is an open source with a large community, offering libraries for more advanced functionalities. While, Scala uses most of the JVM libraries, utilizing Java’s extensive tools.

These libraries make it more difficult for developers to choose any one of them, as both can be used to build highly functional applications.

8. Developers Community and network  

Python is much larger than Scala, in terms of usage, developer’s network, and popularity. Python is preferred by 51% of developers with more than 8 million people contributing to improve it. On the other hand, Scala has a low preference and is loved by 2.6% of developers with 900k developers in the community. 

But, that does not mean Scala is outrated. It has helped brands scale efficiently while ensuring high performance. 

8. Integrations and third-party support 

Python has an extensive range of third-party libraries and integrations. You can simply use Python with many different tools and technologies. From developing simple to complex web applications to dynamic mobile apps, and AI/ML integrations- everything is possible with Python tools. You can use it with databases, web APIs, and even other languages, making it very versatile for a wide range of projects.

Scala also integrates well, especially with the Java ecosystem. Since it runs on the Java Virtual Machine (JVM), it can easily use Java libraries and tools. This makes Scala a great choice for big data projects and enterprise applications, like those using Apache Spark. But it does not have third-party libraries for areas, like web development or machine learning, giving Python a leg up.

9. Scalability 

Python is better for small-scale projects, while Scala is more suited for larger projects. This is because Python lacks built-in features for handling scalability, while Scala is designed for easy, low-latency scalability. In fact, the name “Scala” stands for “scalable language,” and its ability to scale is one of the main reasons large companies choose it.

Scalability also depends on how the system is set up. Python is great for serverless scalability, meaning it can handle flexible, on-demand growth. On the other hand, Scala typically requires more memory and needs a dedicated environment like the Java Virtual Machine (JVM). While Python can still handle large projects, Scala is often the better choice for scalable systems, especially when using microservice architecture.

10. Scala vs Python for Data Engineering

For data engineering, both Python and Scala have their advantages, but they’re often used for different types of tasks. Data engineering involves several tasks-

• Data Collection
• Data Integration
• Data Cleaning
• Data Transformation
• Building Data Pipelines
• Data Storage
• Performance Optimization
• Data Security
• Monitoring and Maintenance

Python is a popular choice for data engineering. It offers libraries for tasks like cleaning data, automating workflows, and managing data pipelines. For example- 

• Pandas to work with data, 
• Airflow to schedule and manage workflows. 
• You can also integrate Python with cloud platforms and different data storage systems.
• Python streamlines the development of data pipelines, ML workflows, and data applications.

Scala is the go-to choice for handling big data. Apache Spark, a tool for processing large datasets, is built in Scala and runs faster when used with Scala. You can use it for enterprise-level systems and projects to process large volumes of data quickly and efficiently.

11. Scala vs Python for Data Science

“Data science is the field that uses techniques, algorithms, and processes to gather data from structured and unstructured data. Data scientists clean, process, and analyze them to solve problems or answer important questions, using ML models or statistical analysis.”

Python and Scala are important for Data Science, solving different purposes. It helps data scientists to clean data, analyze it, and build models for predictions. 

When it comes to processing big data, Scala is a great choice. However, Scala doesn’t have as many ML libraries as Python, so it’s not as widely used for general data science tasks.

12. Scala vs Python for Spark

Python is popular for working with Apache Spark because it’s easy to use and has many tools for data science. However, Python is slower than Scala for big data tasks because it requires more processing. It also needs extra libraries to work with Hadoop’s file system (HDFS), which adds complexity. Still, Python is widely used because it’s simple and has many libraries for analysis and machine learning.

Scala is faster than Python for big data tasks. Since Spark is built in Scala, it works more efficiently and handles large data better. Scala also connects easily with Hadoop and HDFS using Java APIs, while Python requires additional libraries. Scala is better for processing large-scale data and complex tasks, but it can be harder to learn than Python.

About 75% of the Spark codebase is Scala.

13. Python vs Scala for Backend Development

Python is a popular choice for backend development due to frameworks like Django and Flask that make it easy to create and manage web apps. You can use it with databases and APIs. However, it may not be as fast as other languages when handling very large or complex systems.

Scala is better for backend systems that need to handle a lot of data or traffic. It uses Java Virtual Machine (JVM) for faster and more efficient large-scale applications. You can use it for real-time data processing and building systems that need to scale well. It’s a great choice for projects where performance and reliability are very important.

14. Scala vs Python for Big Data

Scala is often the better choice for big data because it works directly with Apache Spark, a popular tool for processing large datasets. Since Spark is written in Scala, it works even faster with it to handle large volumes of data, improving performance due to Java Virtual Machine (JVM). You can use Python with Hadoop alongside Spark for big data storage and processing.

You can use Python in big data using PySpark, a Python API for Spark. 

While Python offers many data science libraries, it is slower than Scala for big data tasks due to its dynamic nature. Python requires more processing time, making it difficult to handle very large datasets. 

15. Popularity 

From the below image, it is clear the popularity and usage of Scala is more than Python. More websites have used Scala. 

Also, if we look at the most in-demand coding languages across the globe, Scala takes over Python.

16. Brands who prefer Python and Scala

17. Scala vs Python: Developers Cost and Salary

The salary of Scala and Python developers can vary based on factors like experience, location, and industry. However, here is a general comparison.

Scala developers tend to earn slightly higher salaries due to the demand for their skills in high-performance systems and big data processing (e.g., with Apache Spark). Python developers are also in high demand, especially in fields like data science, machine learning, and web development, but their salaries may be somewhat lower on average compared to Scala developers, particularly in non-specialized fields.

Final Thoughts- Which One to Choose- Scala or Python

Now, that we have explained all possible differences that will help you understand when to use which language. Python and Scala both are promising languages for developing advanced applications but are suited for different use cases. 

I personally recommend using both sides by side leveraging the capabilities of both languages and building smooth, scalable, and flexible applications that meet future market demands. 

Big companies like Google, Spotify, and Instagram use Python because it’s flexible and powerful. Scala is also used by companies like LinkedIn, Reddit, and Twitter, especially for big data, backend development, and web design. Scala’s combination of object-oriented and functional programming makes it great for data processing and scripting.

There’s no “right” answer between Python and Scala. It depends on your project needs. If you’re unsure, feel free to reach out for help!