It has been observed so often that people or organizations don’t focus on selecting the right language before working on any project. However, there are certain criteria to look into before going ahead like a perfect blend of data, right implementation, accuracy, data models, and so on. The point is working on spark gives you some benefits and opens doors for many different coders like Java, on the other hand, people who are sticking with Python might have to face some pull-offs.
That’s why we came up with this article in which we will try to make things more clear and more transparent and will also be sharing a list of features-cum differentiation which will surely help you to pick the right programming language for you while working with Apache Spark.
In this article, we will be sharing our top 2 languages while working with Apache Spark, so let’s check them simultaneously:
Since we’re talking about Scala, how can we forget Spark? In fact, Apache spark was written primarily on Scala only, therefore each function is well mapped for its developers. Scala is indeed the best go-to language for Apache Spark. It was designed by Martin Odersky in 2001. Although it’s not an old school language but trusts us this, Scala has gained enormous popularity in a very short span of time. Scala comes with a hybrid programming language which states that it can work with both functional and object-oriented programming languages. In some way, there’s no denying that it is a next-level Java programming language. Thus, it can be a good fit for those who have prior knowledge of Java. Now, let’s dig a bit more to see what else it carries with itself that makes it special while using with spark:
- It can defeat any of its rivals when it comes to performance, Scala offers supreme speed in both processing and analyzing data.
- It enables developers to write the clean designs of spark applications and is being considered a statically typed language.
- Due to its procured adaptability, it can even work on real-time data, and on the other end, the processing is very quick.
- With the help of Scala, it is possible and much easier to build big data applications despite holding complexity.
This is one of the most popular languages so far in the field of data science among data scientists around the world and was firstly introduced by Guido van Rossum in late 1991. If you go with the stats, so far it has gained the top spot when it comes to popularity and was initially designed as a response to the ABC programming language of what we know today as a functional language in a big data world. Today, almost every data analysis tool, machine learning, data mining, and manipulation library are operated heavily using this language. It carries good standard libraries with simple syntax. Besides this Python also offers some more resilient features which you should look into it before moving ahead:
- If you’ll look up the internet, you might find many other supportive languages for Apache spark but Python is considered the easiest to understand, and creating schemas, interacting with a local file system, or calling REST API is much easier to perform with python while working in spark.
- It is also called an interpreted language which means that all the codes inside it can be converted back to bytecode which can later be executed back in Python virtual machine.
- Working with Python is way easier for those programmers who have knowledge of SQL or R.
- Python offers an extensive set of libraries that includes string processing, Unicode, or internet protocols (HTTP, FTP, SMTP, etc.) and can easily run on different OS such as Linux, Windows, and macOS.
We’ve seen both programming languages one by one along with their features. Now is the time to take a quick look by comparing both languages for better clarity.
Quick Comparison (Python Vs Scala): Which one to pick while working with Apache Spark?
- If we talk about complexity in programming then working with python is much easier and being an interpreted programming language, a developer can easily compile any code and re-edit it by using a text editor and the same can be executed accordingly whereas working on Scala for this parameter can be a tough call and one cannot simply re-edit the text and execute the codes for compilation.
- Talking about execution speed, Scala offers a superior speed as compared to python. This is because Scala is derived from Java and thus it also uses JVM (Java Virtual Machine) for execution and it also enables it to work seamlessly.
- Being a simple, open-source, general programming language, Python offers simple syntax and less coding, on the other hand, Scala, being a functional program comes with a lot of functions and features which makes it far more typical to work on.-1
- If working on a large project, due to its static nature, Scala is a perfect fit for type checking during its compilation whereas being dynamic types in nature, Python is not that scalable and can only and only fit with small segment projects.
- As we’ve discussed above, Apache spark is being written on Scala because of its scalability over JVM and so it offers accessibility to all the latest features of the spark that is not the whole, but it all depends upon what your requirement is. Let’s say you need better graphical visualization for your project so for that Pyspark is best and that can’t be replaced by either Scala or spark.
Choosing the best language for Apache Spark is not that typical, only a handful of key languages are available out there. Besides if you’re familiar with Java then working with Scala can be a perfect fit for you and on the other hand, if you want to go simply straight with less complexity then python is the answer. At last, it all depends upon your prior knowledge and usability wherever you’ll be applying inside any project. Since, we’ve tried to sort things out by classifying the features and face-to-face comparison but still, what the best you can also do here is create a list of issues in pointers scaling them from usability to learning curve and once you’re done, you’ll surely get the answer for picking up the right programming language for Apache Spark. Also, Java could be considered while working with Apache Spark.