Scala and Python are both popular programming languages that are used in big data projects. Python is a high-level, interpreted language with a simple and clean syntax. It’s most frequently used in data science, data engineering, and web development. Scala, on the other hand, is a statically typed, functional programming language that runs on the Java Virtual Machine (JVM) and is well suited for use in big data processing, distributed systems, and parallel programming.
When considering Scala vs. Python, it’s important to recognize that these two programming languages can be used together quite effectively. In this article, we’ll take a close look at Scala and Python, exploring their similarities and differences. We’ll also explain where each shines so you can choose the right one for your next project.
Scala vs. Python: How Do They Compare?
Although there are some similarities, Scala and Python are fundamentally different in terms of their design, syntax, and intended use. Here’s an in-depth comparison of each language.
Usage
Python is a general-purpose programming language that’s intuitive to learn, easy to use, and ideal for many big data projects. It’s earned its reputation as the multi-tool of programming languages and is highly capable in a range of use cases, including scientific and numerical computing applications, major back-end development projects, and building powerful machine learning algorithms,
Scala is a relative newcomer with a more focused value proposition. Its intended purpose is referenced in its name: Scala is designed for scalability, making it ideal for powering big data systems. Although Scala can be used in a project of any size, its primary use case is for building large, data-intensive, distributed applications and systems. Unlike Python, Scala allows developers to tap into Java’s entire library ecosystem and use Scala and Java interchangeably.
Performance
Python is an interpreted language that requires an interpreter to read and execute the code. This process requires additional compute resources that can bog down performance. In contrast, Scala is a compiled language; written code is compiled into byte code before being run on the JVM. This distinction gives Scala a major advantage in performance, as it is up to ten times faster than Python for certain use cases such as large-scale data processing and analysis.
Scalability
When it comes to maintaining performance at scale, Scala has the edge. As a statically typed language, its variable types are known at compile time. This allows Scala code to execute more quickly using less memory. In contrast, Python is dynamically typed, which requires the interpreter to assign variable types at runtime based on the variable's value at the time. This drags down performance and is especially noticeable in large-scale data processing.
Security
Statically typed languages follow the principles of type safety, a series of built-in controls designed to prevent type errors. Scala is a statically typed language and supports quick bug and compile-time error detection. Statically typed languages are considered more secure because they can identify potential type errors before they’re incorporated into the program. Although Python has a high degree of type safety built in, it’s a dynamically typed language, making it more prone to coding errors and bugs.
Concurrency
Concurrency allows several tasks to be executed at the same time, enabling better memory management and more efficient data processing. With Scala, developers can write code with multiple concurrency primitives to support running several tasks in parallel. Python does not support concurrency, so it requires additional time and compute resources each time a new code is deployed.
Learning curve
Scala is relatively easy to learn, but its more advanced features demand a significant time investment to master. In contrast, Python is one of the most beginner-friendly programming languages with syntax that closely resembles the English language. Because of this, Python is often the first language many data engineers, developers, and data scientists learn.
Community support
Python boasts an enormous developer community with extensive online resources to support everyone, from beginner to advanced user. With a large collection of frameworks and libraries, Python developers are spoiled. Scala is a newer language with fewer use cases. As a result, Scala’s developer community and collection of frameworks and libraries are much smaller.
Where Python and Scala Each Excel
Understanding where Scala and Python work best can help developers make better choices about which language to use for a specific project. Here are use cases where each excels.
Python use cases
Data Engineering
Python is one of the top languages for many “big data”-influenced data engineers. This is especially true for data engineers familiar with frameworks such as Spark and Hadoop/MapReduce. Python’s popularity is primarily due to its rich ecosystem of open-source libraries that accelerate speed of innovation by reducing the development effort of data pipelines for machine learning workflows and data applications.
Machine learning and artificial intelligence
Python’s simplicity and versatility make it popular for machine learning and artificial intelligence projects. With a strong library ecosystem, a platform-independent design, and an active developer community, Python is quickly becoming the language of choice for these use cases.
Data app development
Using Streamlit, developers can quickly build and deploy powerful data apps with no front-end development experience. Streamlit is an open-source Python library that’s intuitive and easy to use, significantly speeding up development of data apps.
Data analysis
Python has become the go-to programming language for data analysis. Its many libraries and tools are purpose-built for handling and manipulating data, seamlessly executing tasks including data collection, analysis, numerical calculations, and data modeling.
Scala use cases
Data processing
Scala is built for large-scale data processing. By facilitating interaction between distributed databases and enabling parallel processing, Scala is able to process enormous amounts of data quickly and efficiently.
Stream processing
Scala also offers a variety of tools and frameworks supporting stream processing. One example is Apache Flink, a renowned open-source stream processing framework. Additionally, Apache Kafka is a popular tool for creating real-time data pipelines and is often used in conjunction with Scala and Flink to develop stream processing applications.
Machine learning
With support for functional programming and Java interoperability, Scala is ideal for use in machine learning. MLib, a popular machine learning library used for data preprocessing, model training, and making predictions, is written in Scala.
Pairing Scala with Python
Although these two languages are distinctly different, there are several instances where they can work together. ScalaPy, an API that enables interoperability between Scala and Python, makes it possible for developers to use Python libraries in Scala. With cross-platform interpreter embedding, developers can integrate Python into existing JVM applications or compile directly to native code. And thanks to ScalaPy’s automatic conversions between Scala and Python types, developers can mix Scala and Python values naturally.
Build With Scala and Python in Snowflake with Snowpark
Snowpark is a developer framework for Snowflake that brings data processing and pipelines written in Python, Java, and Scala to Snowflake's elastic processing engine. Snowpark enables intricate data transformations and manipulations to be executed entirely on a single platform within the Snowflake Data Cloud. With Snowflake, you can unleash the complete potential of your data and drive improved insights and decision-making.