Big data is king and there is no denying that it works wonders for practically any sphere of business. However, choosing the right tools to dig into the information and produce comprehensive results can be overwhelming.
The choice of a certain programming language to manipulate and access the data poses a particular challenge. Numerous options stand before Big data specialists causing certain friction as it is hard to identify the appropriate one.
Let’s focus on the most demanded languages that drive the majority of Big data operations in 2019.
Python is used in a variety of use cases due to its general-purpose nature. And Big data programming provides a robust expression of its productivity. The language is ideal for building easy-to-use systems due to its interactive coding and scripting potential. Even for newcomers, Python is easy to master and read, which makes this language so widespread these days.
Numerous Python-based libraries are coming to the rescue for various tasks, be it machine learning or data analysis. The language is compatible with Spark and Hadoop, allowing you to build analytical solutions based on the data at your disposal.
Numerous companies use Java programming to power Big data applications. Running on Java Virtual Machine, the language is accessible from numerous frameworks including Kafka, Beam, MapReduce. Moreover, one can access the whole bunch of tools and libraries for better productivity within the Java ecosystem.
Java is flexible and its general-purpose nature allows one to build various systems for desktop and mobile platforms.
Java skills are at the forefront of developers’ expertise, yet the language has certain limitations. It doesn’t support iterative development like Python and its verbose nature is challenging for some specialists. To complete one task, one has to write more lines of code when compared to Python, for example.
Running on Java virtual machine, Scala can easily access Java-based tools and libraries. The language powers Apache Spark for effective data processing and cluster computing. Scala’s libraries are not as varied if we compare them to Python, yet they are able to build solutions for machine learning and data analysis.
Scala allows programmers to save time as it is less verbose than Java. However, to master the language, one has to undergo a complicated learning process to develop strong practices of using the language. That is why some developers prefer to choose less demanding options.
Data manipulation and analysis is easy with R. Basically, this language is used by statisticians to build models and algorithms for effective data analysis. The R language is not ready for production once the code is written. This means that to deploy the code, one has to call production-ready languages like Java.
A variety of R-based packages is staggering when it comes to data visualization and analysis. However, the language has limited capacities for general-purpose applications. That is why many consider it as the tool for data science solely.
Julia is one of the fastest-growing languages with a robust community. Designed for high-performance environments, Julia is incomparable to Python or R when it comes to speed of processing. That is why many cloud computing platforms are powered by this language.
Numerous packages including mathematical libraries and general-purpose tools provide excellent functionality when it comes to Big data applications. Julia can also extend the basic functionality and provide native bindings to external libraries (Python, Java, R, etc).
Many see Julia as an indispensable part of expertise for Big data. However, the language will have to prove its maturity in the coming years.
Are you ready to justify the purpose of each language in your expertise? If not, your Big data project is deemed to fail. Choosing the right language for Big data depends on a certain case you plan to develop. Whether you build a streaming solution or employ machine learning algorithms, you will have to use different tools to power your exact application.
What language do you see on top of the list? What do your Big data specialists prefer? Do let us know in the comments below 🙂