Today, data is one of the most important assets a business can have. Because of large-scale data collection, businesses today are faced with the challenges of working with so-called Big Data, meaning data sets that are too large or unwieldy for the application of traditional data analysis techniques. To work competently with Big Data, engineers and data scientists must use proprietary techniques and an ever-changing array of analysis methods. Like any other basic computational function, though, these techniques are executed using common programming languages. Here are the top three programming languages that will help you analyze Big Data more effectively.
Widely liked for its ease of use, Python is currently the most commonly taught programming language for beginning computer science classes in American universities. Python is also a powerful programming language for dealing with unusually large data sets. The language offers fast processing and is open source, meaning that users don't have to pay to get started with it. In addition, Python comes with a wide range of tools meant specifically for analyzing large, complex data sets. Python is also capable of dealing with non-numeric data, such as data from pictures, videos and audio recordings, in a way that few other languages are well-equipped to do.
As in many other programming applications, Java is a go-to language for many people who work with Big Data. Java is a ubiquitous language among programmers, making it a good language for writing new software, applications and other tools in. In addition to the fact that it can be used by almost any competent programmer, Java includes specific frameworks that give it added functionality for working with Big Data. The most important of these is Hadoop, a framework that was developed specifically for data-heavy applications such as data catalogs. Many data catalog products have proven themselves to be a very effective framework for working with large datasets sets. This allows businesses to better work with their IT department analyzing their data. Another advantage of Java is the fact that it is expected to remain in use for the foreseeable future, meaning that programmers who learn to analyze Big Data using it will have a sustainable skill that will be in demand for years to come.
R is considered to be one of the most important languages ever developed for data analysis. Originally released in 1993, R was designed specifically for handling and analyzing large data sets. Since then, R has remained a critical tool for professionals who work with data. Today, R is routinely rated as the most-used programming language by data scientists and analysts. One of the most important advantages of using R is the fact that there are a massive number of open source R packages that have been developed over the years for handling specialized types of data. R also facilitates easy parallel processing, which allows a computer system to execute several calculations simultaneously. This kind of parallel computing is extremely useful when working with datasets.
Using any of these languages, you can work effectively with Big Data to gain useful insights for your business. Keep in mind that you will likely be able to apply a programming language you are already comfortable with to Big Data more effectively than you could one that you are just learning. If you already know Python well, for instance, you will probably be better off working with its Big Data tools than trying to learn the Hadoop framework in Java or the various data analytics packages available in R. In the end, any one of these languages, as well as several others, can be used for Big Data analytics.