Top 5 programming languages for crunching Big Data effectively

One of the most important decisions that Big Data professionals have to make, especially the ones who are new to the scene or are just starting out, is choosing the best programming languages for big data manipulation and analysis. Understanding the Big Data problem and framing the architecture to solve it is not quite enough these days – the execution needs to be perfect as well, and choosing the right language goes a long way.

The best languages for big data

In this article, we look at the 5 of the most popularly used – not to mention highly effective – programming languages for developing Big Data solutions.

Scala

A beautiful crossover of the object-oriented and functional programming paradigms, Scala is fast and robust, and a popular choice of language for many Big Data professionals.The fact that two of the most popular Big Data processing frameworks in Apache Spark and Apache Kafka have been built on top of Scala tells you everything you need to know about the power of Scala.

Scala runs on the JVM, which means the codes written in Scala can be easily used within a Java-based Big Data ecosystem. One significant factor that differentiates Scala from Java, though, is that Scala is a lot less verbose in comparison. You can write 100s of lines of confusing-looking Java code in less than 15 lines in Scala. One negative aspect of Scala, though, is its steep learning curve when compared to languages like Go and Python, and this may put off beginners looking to use it.

Why use Scala for big data?

• Fast and robust
• Suitable for working with Big Data tools like Apache Spark for distributed Big Data processing
• JVM compliant, can be used in a Java-based ecosystem

Python

Python has been declared as one of the fastest growing programming languages in 2018 as per the recently held Stack Overflow Developer Survey. Its general-purpose nature means it can be used across a broad spectrum of use-cases, and Big Data programming is one major area of application.

Many libraries for data analysis and manipulation which are increasingly being used in a Big Data framework to clean and manipulate large chunks of data, such as pandas, NumPy, SciPy – are all Python-based. Not just that, most popular machine learning and deep learning frameworks such as scikit-learn, Tensorflow and many more, are also written in Python and are finding increasing application within the Big Data ecosystem.

One drawback of using Python, and a reason why it is not a first-class citizen when it comes to Big Data programming yet, is that it’s slow. Although very easy to use, Big Data professionals have found systems built with languages such as Java or Scala faster and more robust to use than the systems built with Python.

However, Python makes up for this limitation with other qualities. As Python is primarily a scripting language, interactive coding and development of analytical solutions for Big Data becomes very easy. Python can integrate effortlessly with the existing Big Data frameworks such as Apache Hadoop and Apache Spark, allowing you to perform predictive analytics at scale without any problem.

Why use Python for big data?

• General-purpose
• Rich libraries for data analysis and machine learning
• Easy to use
• Supports iterative development
• Rich integration with Big Data tools
• Interactive computing through Jupyter notebooks

R

It won’t come as a surprise to many that those who love statistics, love R. The ‘language of statistics’ as it is popularly called as, R is used to build data models which can be used for effective and accurate data analysis.

Powered by a large repository of R packages (CRAN, also called as Comprehensive R Archive Network), with R you have just about every type of tool to accomplish any task in Big Data processing – right from analysis to data visualization. R can be integrated seamlessly with Apache Hadoop and Apache Spark, among other popular frameworks, for Big Data processing and analytics.

One issue with using R as a programming language for Big Data is that it is not very general-purpose. It means the code written in R is not production-deployable and generally has to be translated to some other programming language such as Python or Java. That said, if your goal is to only build statistical models for Big Data analytics, R is an option you should definitely consider.

Why use R for big data?

• Built for data science
• Support for Hadoop and Spark
• Strong statistical modeling and visualization capabilities
• Support for Jupyter notebooks

Java

Last, but not the least, there’s always the good old Java. Some of the traditional Big Data frameworks such as Apache Hadoop and all the tools within its ecosystem are all Java-based, and still in use today in many enterprises. Not to mention the fact that Java is the most stable and production-ready language among all the languages we have discussed so far!

Using Java to develop your Big Data applications gives you the ability to use a large ecosystem of tools and libraries for interoperability, monitoring and much more, most of which have already been tried and tested.

One major drawback of Java is its verbosity. The fact that you have to write hundreds of lines of codes in Java for a task which can written in barely 15-20 lines of code in Python or Scala, can turnoff many budding programmers. However, the introduction of lambda functions in Java 8 does make life quite easier. Java also does not support iterative development unlike newer languages like Python, and this is an area of focus for the future Java releases.

Despite the flaws, Java remains a strong contender when it comes to the preferred language for Big Data programming because of its history and the continued reliance on the traditional Big Data tools and frameworks.

Why use Java for big data?

• Traditional Big Data tools and frameworks are written in Java
• Stable and production-ready
• Large ecosystem of tried and tested tools and libraries

Go

Last but not the least, there’s Go – one of the fastest rising programming languages in recent times. Designed by a group of Google engineers who were frustrated with C++, we think Go is a good shout in this list – simply because of the fact that it powers so many tools used in the Big Data infrastructure, including Kubernetes, Docker and many more.

Go is fast, easy to learn, and fairly easy to develop applications with, not to mention deploy them. More importantly, as businesses look at building data analysis systems that can operate at scale, Go-based systems are being used to integrate machine learning and parallel processing of data. It is also possible to interface other languages with Go-based systems with relative ease.

Why use Go for big data?

• Fast, easy to use
• Many tools used in the Big Data infrastructure are Go-based
• Efficient distributed computing

There are a few other languages you might want to consider – Julia, SAS and MATLAB being some major ones which are useful in their own right. However, when compared to the languages we talked about above, we thought they fell a bit short in some aspects – be it speed, efficiency, ease of use, documentation, or community support, among other things.

Let’s take a quick look at the comparison table of all the languages we discussed above. Note that we have used the ✓ symbol for the best possible language/s to help you make an informed decision. This is just our view, and that’s not to say that the other languages are any worse!

So…which language should you choose?

To answer the question in short – it all depends on the use-case you want to develop. If your focus is hardcore data analysis which involves a lot of statistical computing, R would be your go-to language. On the other hand, if you want to develop streaming applications for your Big Data, Scala can be a preferable choice. If you wish to use Machine Learning to leverage your Big Data and build predictive models, Python will come to your rescue. Lastly, if you plan to build Big Data solutions using just the traditionally-available tools, Java is the language for you.

You also have the option of combining the power of two languages to get a more efficient and powerful solution. For example, you can train your machine learning model in Python and deploy it on Spark in a distributed mode. Ultimately, it all depends on how efficiently your solution can function, and more importantly, how fast and accurate it is.

Which language do you prefer for crunching your Big Data? Do let us know!