Most-Used Programming Languages to Power Big Data and Data Science
Nowadays, use cases of Big data are numerous. Some of them deal with heavy data analysis while others employ predictive models or handle data streams, etc.
Each case requires a certain set of features that data scientists are eager to implement. Sounds simple, yet there is a trap.
The sheer variety of Big data tools and techniques used in each case is staggering. Developers have to decide on their specialization, and even more so if they’re only at the beginning of their career as Big data specialists. One of the crucial choices is the programming language to manipulate, analyze, and dig deeper into the data.
Adding the right data science programming skills to your toolbox will help you tackle data-related problems without putting a damper on execution.
Let’s consider the most important programming languages for data science and Big data processing that dominate this sphere in 2019.
It is hard to imagine a Big data project without this coding language because of its strong typing and outstanding flexibility. As numerous Big data tools run on JVM, Java skills are necessary to be able to perform precise data analysis. So prior to Hadoop, one has to learn Java first to understand troubleshooting. Java’s portability makes it indispensable on almost any system, be it desktop, or mobile, or web applications.
However, the Java programming language has certain limitations like any other programming language. Java’s verbose nature limits its ability to produce truly sophisticated applications. Scarcity of native data science libraries is also another source of developers’ complaints.
The Python language is your choice when you have to train non-programmers in analyzing Big data and visualizing it for your projects. Ideal for newcomers, this language offers free tutorials that are available to anyone who’s done a little googling. However, Python’s general-purpose nature is not just for entry-level developers. And its features make it useful for data science, too.
What makes Python programming so widespread? It offers mountains of packages available for various tasks ranging from machine learning to data analysis. For example, if your project deals with signal or image processing, the Scipy library is there to your rescue. Do you need to dive into neural networks? Google’s TensorFlow package is accessible from the Python coding language, too.
One of the cons of the Python programming language is that it is relatively slow when compared to its closest rivals. Another reason that intimidates developers is its interactive coding. One has to be aware that just one typo in the line may ultimately affect the entire program. Yet Python machine learning and data analysis capabilities make up for its major limitations.
Handling Big data in the R coding language is a productive way to deliver accurate results and expose the data to end users. Basically, it is a tool that suits well to build statistical models and algorithms. The language includes basic elements that are easy-to-use and manipulate data with.
Last but not least, the R language core comes as open source. It can power virtually any task in Big data manipulation. One can also integrate R into Apache Spark to expand the computer capacity if necessary.
The R programming language is well-suited to deliver data analytics but its code is not production-ready once written. Unlike Python, it is R’s specific design that doesn’t make it a general-purpose programming language. However, R is the choice for data scientists who deal with statistical computing and its graphical representation.
Programming in Scala combines both functional and object-oriented paradigms that act seamlessly to deliver accurate bug-free programs. The Scala language is widely spread among companies willing to integrate their services into the vast Big data ecosystem. The language powers distributed Big data tools such as Spark and Kafka as well as many other Java-based frameworks. Developers can also access Java packages directly from Scala to interoperate with Java code more easily.
The Scala computer language offers comprehensive libraries for machine learning and data analysis. However, they are not as varied as Python or R libraries. Scala programming is also notorious for its steep learning curve. As Scala is based on Java Virtual Machine, some background Java skills could be a plus.
MATLAB performs well in specific use cases that require advanced mathematical calculations. Though its design doesn’t exactly fit general-purpose programming, data scientists can still use its computation and programming potential to tackle daily tasks. Among them are signal processing, matrix computations, scientific graphics, data analysis and visualization, to name but a few.
In general, Matlab’s functional capabilities make it useful for various Big data applications. The language allows one to create programs of different scope and functionality and scale them according to the requirements using the same code. Matlab-based machine learning is a great option for data scientists and data engineers to explore patterns in Big data. One can also leverage Matlab’s ability to build models as well as train and compare them. Best for specific use, the Matlab language is not your tool for more general tasks. However, many combine it with Python to access the latter’s libraries.
There are other popular programming languages that cater for Big data and may be useful in a variety of tasks.
Other popular options for your Big data projects
The Julia programming language welcomes Big data applications and is vastly used for cloud computing and its concurrent functions. Julia comes with native packages, yet it can access external mathematical libraries and data manipulation tools, too. From the year of its inception, the Julia computer language was positioned to combine Python’s dynamic nature and the speed of compiled languages like C.
Julia is still in its early years but is seen by many as a major tool for Big data applications. It is not a mature language with an established community and available tutorials. Yet it is growing rapidly and is widely used by numerous developers in large projects.
The C++ programming language is an established language with a large set of libraries and tools that are ready to power Big data applications and distributed systems. In the majority of cases, C++ is used to write frameworks and packages for Big data. This coding language also offers a number of libraries that assist in writing deep learning algorithms. With sufficient C++ skills, it is possible to perform virtually unlimited functions. Yet C++ is not the language you can easily learn as one has to master its 1000+ pages of specification and almost 100 keywords.
If Java has been there for more than 21 years, Go programming has emerged relatively recently. Now, this language powers data-driven industries due to its jaw-dropping productivity and simplicity of use. The Go programming language offers a wide spectrum of libraries and tools for reading various parts of the data. Despite its immaturity, many are optimistic about this language and see it as the tool for parallel programming. Time will show if Go stays up to the hype.
Which programming language to learn?
While it is hard to name the best language for data science and Big data, each choice should be justified by exact purposes. Consider high-performance compiled languages like Java to generate a production-ready code in intensive environments. Python’s various tools will be of help to conduct an in-depth exploration of the data and machine learning applications. If you want to build a solution to process data streams, use Scala as it adapts well to large amounts of data in distributed systems. R is the most preferable option for handling statistical models that support complex calculations while MATLAB is an obvious choice for intensive quantitative applications.