Which one should I learn: Python or Scala?

Python’s syntax resembles the English language, creating a more comfortable and familiar environment for learning. Python’s simplicity and simple to learn syntax make it the ideal choice for developing a desktop graphical user interface applications, web applications, and websites. Its emphasis on readability makes it a cost-effective option, particularly in terms of maintenance. Further, Scala is statically typed, helping programmers avoid bugs, leading to more error-free, concise, and readable code.

• Scala is a statically typed language, making it easier to find compile-time errors.
• Scala is a hybrid functional programming language because It has both object-oriented and functional programming features.
• Scala is not a difficult language to get started with, but it is considered a complicated programming language to master.
• That is where the Python and R languages start bleeding on a Spark cluster.
• One reason Scala wins on performance is that it is a statically typed programming language and Python is a dynamically typed programming language.

This is also something that we use all the time while coding, luckily there is a only a slight difference between the two languages. Another striking difference is how we define the variables in the two languages. Xavier is an entrepreneur, project manager, architect, trainer and developer who applies his experience, passion and desire for results with a focus on simplicity and common sense. He works primarily with ASP.Net, iOS, search applications and holds multiple Microsoft and Scrum Alliance Certifications.

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. 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.

Developing Apache Spark applications: Scala vs. Python

Further, this general-purpose approach paired with its intuitive, easy-to-understand code syntax has made Python one of the most popular coding languages for beginners to programming. The Spark creators claimed that this kind of coordination is not a big deal and for a typical Spark job, the communication effort has a negligible impact on performance. However, if they need to move data between these workers process, it may severely impact the performance. If you have a Python function that needs to work on each row of the partition, then the JVM worker must serialize the data and send it to the Python worker. The Python worker will transform that row and serialize the output back to the JVM.

The concurrency feature means Scala enables better memory management and quicker data processing. Python is an interpreted language, which is one of the most popular choices for modern software development, along with JavaScript and Ruby. Furthermore, Python’s ecosystem is an ideal resource for machine learning and artificial intelligence , two of today’s increasingly deployed technologies.

Python Tutorial

Currently, there is a trending debate about the different benefits of Python vs Scala, and the pros and cons of each language. Scala handles concurrency and parallelism very well, while Python doesn’t support true multi-threading. The best way to answer the “Scala vs. Python” question is by first comparing each language, broken down by features. As mentioned earlier, many influential businesses and organizations use or have migrated to Scala. Additionally, Scala has a brighter future in many ways.

Hence, analyze and learn all the different artifacts of both Python and Scala before deciding on which one language. As per task complexities, python has huge libraries, whereas, for Scala, it has small libraries. It depends on how your codebase is divided in packages. 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.

And although we emphasized Python’s fit for small-scale apps, scripting, and data processing, don’t write off Python as a language fit only for small-scale data science applications. Python fuels high-performance systems, including many of today’s favorite services such as Netflix, Twitter, and other large-scale operations. It’s perfect for back-end programming, scripting, software development, and web design. Scala, which stands for “scalable language,” is a high-level programming language that combines functional programming and object-oriented programming. It runs on the JVM and is compatible with existing Java code and libraries. Python is an interpreted, object-oriented, high-level programming language.

Scala vs Python – TypeSafety

Now working on a project of streaming data, it is important to choose the good tools in order to accomplish the project. The first question after ingesting Data is how to process those Data? One answer is by using Spark but Spark has multiple API’s. For implementations, the choice is in your hands which language to choose for implementations but let me tell you one secret or a tip; you don’t have to stick to one language until you finish your project.

However, Scala is a distinctly different language than Python. In comparison of the other languages, scala code is error-free, short and readable. It is simple to use for writing, compiling, running programs and debugging. The scala source code is developed in a unique manner that its compiler can interpret the Java classes.

Therefore Scala is available for all platforms that are supported by JVM, which includes the same platforms as listed for Python. Hadoop is Apache Spark’s most well-known rival, but the latter is evolving faster and is posing a severe threat to the former’s prominence. Many organizations favor Spark’s speed and simplicity, which supports many available application programming interfaces from languages like Java, R, Python, and Scala. Python, however, is perfect for scripting on the fly or piecing together a rapid prototype, leading to a very lean, cost-optimized approach that saves time and resources.

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. 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. 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.

So, all and all, Python being more prone to errors and requiring more computing resources at runtime all point to the fact that Scala is superior in the scalability quadrant. However, the conversation around scalability Why a Full-Stack BI Architecture Matters doesn’t end here. But if we look at the bigger picture, Scala is designed to be less prone to bugs as it is statically typed. Because application errors and compile-time errors equal time and money.

Learn Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. You will even learn how to overcome MapReduce’s limitations by using Spark. To this point, Python, as a scripting language, has to be interpreted from plain text at runtime by the compiler. This takes computing resources at runtime – a significant drawback of Python in scalability.

Differences between Python and Scala

Python is a mature language, and its usage continues to grow. But in the case of Scala, it doesn’t have widespread Cryptocurrency Exchange Script Bitcoin Exchange Script use or knowledge base. In the case of Python, the low level can be achieved by extending using C and C++.

Python and Scala Comparison Table

You get powerful extensibility via the support of custom processors for Scala and Pyspark which helps you better operationalize your code. On the other hand, Scala isn’t considered a hard programming language per se. However, Scala is How To Become A Python Developer Full Guide regarded as a hard programming language to master. When you consider the cost of Python and Scala regarding both team resources as well as the cost of potential bugs and security vulnerabilities, we believe Scala and Python are tied.