Nowadays, use cases of Big Data are numerous. Some of them deal with heavy data analysis. Others employ predictive models or handle data streams. 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, if they’re at the beginning of career. 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.
It is hard to imagine a Big Data project without this programming language. That's 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. It works for desktop,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. 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. It ranges 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 this programming language is that it's relatively slow. Another reason that intimidates developers is its interactive coding style. One should know that just one typo in the line may 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. However, 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. R is the choice for data scientists who deal with statistical computing and graphical representation.
Programming in Scala combines both functional and object-oriented paradigms. These paradigms act seamlessly to deliver accurate bug-free programs. Scala 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 JVM, 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 in 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, etc.
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. Additionally, it scales them according to the requirements using the same code. Matlab-based ML is a great option for data engineers to explore patterns in Big Data. One can also leverage Matlab’s ability to build, train and compare models.
Best for specific use, the Matlab language is not your tool for more general tasks. However, many combine it with Python to directly access the numerous libraries available in the latter.
There are other popular languages that cater for Big Data and may be useful for many tasks.
Julia welcomes Big Data applications and is vastly used for cloud computing. This programming language comes with native packages. Yet it can access external mathematical libraries and data manipulation tools, too. From the year of its inception, Julia was positioned to combine Python’s dynamic nature and the speed of compiled languages like C.
Julia is still in its early years. Nonetheless, it's 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.
C++ is an established language with a large set of libraries and tools. It is ready to power Big Data apps and distributed systems. In most 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 language skills, it is possible to perform virtually unlimited functions. Yet C++ is not the language you can easily learn. 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. This language now powers data-driven industries due to its jaw-dropping productivity and simplicity of use. Go offers a wide spectrum of libraries and tools for reading various data. Despite its immaturity, many are optimistic about this language. Developers see it as the tool for parallel programming. Time will show if Go stays up to the hype.
While it's 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 production-ready code in intensive environments. Python’s various tools help to conduct an in-depth exploration of the data and ML apps. 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. For handling statistical models that support complex calculations use R. And choose MATLAB for intensive quantitative applications.
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