Apache Spark is among the most popular frameworks to analyse big data and deploy machine learning algorithms among data engineers. Although Spark includes APIs for Python, Scala, Java, and R, Python and Scala are the most widely used languages in the data science field.
In this blog, we will draw a comparison between Apache Spark, Python and Scala to help pick the one you should go for.
What is Scala?
Scala is a high-level programming language that is a blend of functional programming and object-oriented programming. It is an acronym for “scalable language.” It is based on the Java Virtual Machine (JVM) and works with current Java programmes and resources.
In comparison to other languages, many developers regard Scala code to be error-free, compact, and understandable, making it simple and easy to write, compile, debug, and run programmes.
- Because Scala operates on the JVM, the Java and Scala stacks can be blended for smooth integration.
- In Scala, we can merge the interface and functionality of numerous characteristics into a single class. In Scala, structural data types are represented by case classes.
- Scala enables generic classes, variance annotations, abstract type members, compound types and more.
- It has a straightforward structure that makes it ideal for huge data processors.
- The Scala Library Index (Scaladex) is a map of all Scala libraries that have been published. A developer can search over 1.75 L Scala library versions.
What is Python?
Python has lately been one of the most widely used programming languages on the globe. It’s applied in a variety of areas, including machine learning, website development, and software testing. It is suitable for both developers and non-developers.
Python is a programming language that is commonly used to create websites and applications, automate operations, and perform data analysis. Python is a general-purpose programming language, which means it can be used to develop a wide range of applications and isn’t tailored to any particular problem.
- Python has a simple syntax and enables modules and packages, which encourages modular and reusable software.
- Python has also been a popular choice among data scientists due to its great efficiency. Python programmes are also simple to debug.
- Thousands of Python tools and frameworks are available to data engineers and data scientists.
- Because of the numerous tools and libraries available, it can assist in the automation of various processes.
What is Apache Spark?
Apache Spark is a comprehensive analytics engine for Big Data processing that is open-source. It’s the go-to platform for batch processing, large-scale SQL, machine learning, and stream processing, with simple built-in modules for each.
Spark is a general-purpose cluster computing platform that can handle large datasets and execute processing tasks quickly. The framework may also deploy data processing activities among a large number of nodes, either alone or in conjunction with other distributed computing technologies.
Apache Spark’s most well-known competitor is Hadoop, however, Spark is moving faster and poses a serious threat to Hadoop’s dominance. Spark’s efficiency and accessibility appeal to many organisations, and it supports a wide range of application programming interfaces (APIs) from languages such as Java, R, Python, and Scala.
Scala Vs Python for Apache Spark
- Software developers must declare object types and variables in Scala because it is an object-oriented, statically typed programming language. Python is a dynamically typed object-oriented programming language that does not need to be specified.
- Scala is 10 times quicker than Python in terms of effectiveness.
- Scala is simple to learn than Python, yet the latter is simpler to grasp and operate with and is generally more user-friendly altogether.
- Scala excels in concurrency and parallelism, whereas Python does not offer true multi-threading.
- In comparison with Python, Scala is more complicated to learn. The syntax and standardised libraries of the latter greatly contribute to the language’s readability.
- Variables with a static type cannot be changed. Python is a dynamically typed language, while Scala is a statically typed language. Because of its static nature, Scala is a superior fit for high-volume applications because it allows for quicker bug and compile-time error checking.
- Python, in comparison to Scala, has a large community from which to garner support. As a result, Python has a larger library of libraries dedicated to various task difficulties. Scala, on the other hand, has a lot of support, but it’s nothing compared to Python.
- Python is more suited to small projects, but Scala is more suitable for large projects.
Which One To Go For?
If one must pick between Scala and Python for Apache Spark, the decision should be made entirely based on the project at hand. Python is usually excellent for smaller projects, while Scala is suitable for larger ones. Scala is used by companies like Netflix and Airbnb, which put up with a lot of data and develop a lot of pipelines. Both have advantages and disadvantages, and a thorough assessment of needs is required before selecting another.