Apache Spark is one of the most popular framework choices among data engineers for analysing big data and deploying machine learning algorithms. Spark has APIs for Python, Scala, Java and R, but Python and Scala have emerged as the most popularly used languages for Spark in the data science industry.
Let us try to understand what makes them so popular and which of the two you should pick for using the Apache Spark framework.
What is Apache Spark
Apache Spark is a multi-language framework for data engineering and machine learning on single-node machines or clusters. It unifies data processing into batches and real-time streaming and can perform Exploratory Data Analysis (EDA) on petabyte-scale data without downsampling. Engineers can execute SQL queries for dashboarding quite fast in this framework. They can also train machine learning algorithms on the laptop and use that code itself to scale up manifolds.
Why is Python so popular?
Named as the TIOBE Programming Language of the Year for the second time in a row, Python is usually the first choice when it comes to programming languages among data scientists. It is an interpreted, object-oriented, high-level programming language along with dynamic typing and dynamic binding.
- Python has a very easy-to-understand syntax and supports modules and packages, encouraging program modularity and code reuse.
- Python has also become a favourite in the data science community due to its high productivity levels. Debugging Python programs is also fairly easy.
- Data engineers and data scientists get hundreds of Python libraries and frameworks to choose from.
- It can help automate different tasks due to the variety of tools and libraries available.
What makes Scala stand out
Scala, on the other hand, pushes data engineers to build a software engineering mindset that can have a long term impact on their careers. It supports functional and object-oriented programming. In addition, its Java Virtual Machine (JVM) and JavaScript runtimes create high-performance systems.
- Java and Scala stacks can be mixed for seamless integration as Scala runs on JVM.
- We can mix multiple traits into a class in Scala to combine their interface and behaviour. Structural data types are represented through case classes in Scala.
- Scala supports generic classes, variance annotations, abstract type members, compound types and more.
- It comes with a simple structure which makes it suitable for big data processors.
- The Scala Library Index (Scaladex) is a representation of a map of all published Scala libraries. A developer can query more than 175,000 releases of Scala libraries.
Which one to go for
If one has to choose between Scala and Python for Apache Spark, the choice should be completely based on the project they are working on. Usually, Python is suitable for smaller projects, and Scala works best for large-scale ones. Companies like Netflix and Airbnb, which deal with huge amounts of data, use Scala and write many pipelines. Both of them have their own pros and cons, and proper evaluation of needs must be done before choosing one over the other.
Speed of performance
Scala is faster than Python due to its static type language. If faster performance is a requirement, Scala is a good bet. Spark is native in Scala, hence making writing Spark jobs in Scala the native way.
Learning the language
Though Scala has been making a name recently, it is not very easy to learn. Compared to that, Python is much easier to grasp. Engineers starting out might find it easier to write in Python than Scala.
Type safety
Scala is static-typed, while Python is a dynamically typed language. Due to its nature, the former is more suitable for projects dealing with high volumes of data.
Python enjoys a larger community support
As Python is the go-to programming language these days, it has built huge community support compared to Scala, whose adaptability is quite small compared to Python.
