The languages in which the best databases in the world are developed

Some programming languages were created to handle large data sets, and a whole ecosystem of libraries and frameworks has developed around them. Other languages are quite new, but work much faster. Together we explore when it’s better to use R and when it’s better to use MATLAB, and why Julia can supplant Python.

Programming is needed for all phases of working with big data, from offloading and cleaning up to designing databases and fine-tuning machine learning algorithms. You can choose any language from this list, but each has its own features and tasks for which it is best suited. If it’s working with a customer database for marketing analytics, the simpler languages are fine, and if it’s serious scientific research, the more complex and precise ones are fine.

R is for statistics lovers

R was created to work with statistics. It allows you to collect and clean data, work with tables, perform statistical tests, different types of analysis and make graphical reports. R is suitable for specialists who are familiar with probability theory, statistical methods and mathematical analysis, so at first glance it may seem complicated because of its intuitively incomprehensible syntax.

In practice, R is used:

for scientific research in a variety of fields; machine learning and neural networks; marketing research.

More than 10 thousand libraries and extensions are created for R. For example, Ggplot2 – for data visualization, Bioconductor – for work with genetic information, and Quanteda – for text analysis.

In addition, R is distinguished from its competitors by high speed of data processing and open source code.

Python is popular and easy to understand

The most popular programming language in the TIOBE ranking. In the work with Big Data Python has established itself as one of the best tools on par with R:

Data scientists use the language to work with machine learning and artificial intelligence. Analysts use libraries and frameworks to process large data sets. Data engineers use it to integrate third-party data solutions.

Several specialized Python libraries have been created to handle data: NumPy for calculations, Pandas for tabular data analysis, and Matplotlib for visualization. In contrast to R, Python in addition to data processing and visualization is actively used to develop websites, applications and other products. It is also considered an easy language for beginners, as it has a clear syntax (we told you how to start learning Python in this article).

For analyzing big data in Python, Victoria recommends using PySpark:

“There’s a PySpark library from the Apache Spark project for big data analysis. PySpark provides a lot of features for big data analysis in Python. It comes with its own shell that you can run from the command line.”

Java is the most versatile

Websites, software and applications are written in Java. It is a multifunctional and cross-platform language, the code in which works equally on mobile devices, consoles or smart home system.

It is positioned as the No. 1 language in the world and is used by about 9 million developers. Many Big Data open-source tools are written in Java (for example, most of Hadoop ecosystem), so developers can create their own data management products based on them. Versatility is a major advantage of Java in Big Data.

Scala is the most underrated

Scala language is not very popular among programmers, in TIOBE rating it is not even in the first twenty. At the same time, in data processing tasks it is much faster than the more popular Python. Scala can quickly process incredibly large amounts of information, so it’s used by Twitter, LinkedIn or Tinkoff.

The Apache Spark framework, important for machine learning and big data analysis, is written in Scala. The framework is part of the Hadoop ecosystem and enables parallel processing of unstructured data in real time. It easily interfaces with Java code and libraries of that language.

C++ is complex but fast

C++ is a general purpose language, meaning that it can be used to solve any kind of programming problem. It is most often used to write operating systems, large games, and software packages such as MS Office or Adobe. In Big Data, too, it is used mostly to create data processing tools rather than to work with them directly. For example, MapReduce, which is now part of the Hadoop ecosystem, was originally written just in C++.

C++ is faster than many competitors (Go, R, or Python). This is especially in demand in machine learning, where you need to process terabytes of data quickly. It is the only language in which data larger than 1 GB can be processed in a second. But it’s also really hard to learn. In 2009, Google created a simple and straightforward language called Go that could handle C++, heavy workloads, and large amounts of data. After that, there was a debate in the development community about whether it was better to learn GoLang or C++.