This article covers the top 5 most important big data languages, like, Scala, R, Python, etc.

The specialists dealing with large data analysis and manipulation have a critical problem regarding the programming language they utilize. These analysts must not only comprehend the problem and design the architecture, but the language also plays a significant role in the execution and implementation of the program architecture.

1. Scala: Scala is a popular programming language among prominent data analysts due to its rapid and robust features. The programming language was developed to provide a crossover between the functional and object-oriented programming paradigms. 

2. Python: Python  has evolved into one of the most adaptable programming languages, capable of being utilized across various applications, including big data processing. Python provides the foundation for several data analysis libraries, such as SciPy, NumPy, and Panda, and the manipulation and cleaning of large data frameworks.

3. R: R is a statistical language based on data models, and it is one of the most successful languages for quantitative data analysis. The programming language includes an extensive library of CRAN packages or a comprehensive R Archives network that aids in processing massive data utilizing the tool repository. 

4. Java: Even though Java is an ancient programming language, it has shown to be one of the most historically executive frameworks used for extensive data analysis and related ecosystems utilized by many Enterprises even now. 

5. Go: Go is the most recent addition to the programming languages used for extensive data infrastructure and associated features. It was created by a group of Google Engineers attempting to create a less laborious language than C++.