Big Data is currently one of the most valuable resources. The amount of data produced by businesses and individuals is increasing at a fast pace, and it is estimated that by 2025 the amount of data can reach up to 175 zettabytes. Python is the finest programming language for handling this Big Data.
Using Python, developers can perform statistical analysis. Besides, the programing language is simple to use and easy to comprehend. There are more than one factors that contribute to the success of Python.
Among the many factors that make Python a successful programming language, an important factor being the language supports data analytics as well as data science. The language Python is used by many important businesses, like Facebook, Quora, Google, Mozilla, and others, to manage their respective data.
Therefore, it is obvious that Python can handle Big Data. This content write-up discusses the different factors that contribute to Python’s handling of Big Data.
Python is an open-source and convenient-to-learn programming language
Python is very much open-source, so developers and programmers can use this language free of cost. If you want to utilize Python for programming purposes, then you can download the latest version of the language from python.org.
Python.org is the official website when it comes to downloading Python programming language. Besides, learning the language is convenient. It is the preferred choice for both seasoned developers as well as students.
Data Engineers, as well as Data Scientists, can use Python for handling Big Data development services without having to know the technical details of the language.
Python is a scalable computer programming language
Python is very scalable when managing big volumes of data, which is essential in Big Data. The language is more adaptable and robust when compared to other computer programming languages like Java. Because of the versatility of the language, it is used in Big Data analytics. Python can handle Big Data more promptly when compared with Java.
Besides, Python is adaptable and very much effective. The programming language helps programmers to write less code and get more work done quickly. The Python computer programming language is thus suitable for Big Data analytics.
Python Offers Several Libraries
Developers utilizing Python computer programming language can use a large variety of modules and frameworks while compiling and executing programs. The frameworks and libraries that are associated with the programming language save time for the developers and programmers, and that helps to boost Python’s popularity.
The language is immensely helpful for data analytics as well as machine learning. Among the many reasons why Python programming language is a good choice for handling Big Data is the presence of a variety of libraries associated with the language and the support these libraries offer to the coder/programmer. The libraries that are part of the Python programming language are as follows:-
The Pandas library is a free software collection that is utilized for handling as well as analyzing data.
The NumPy library is a free software library for doing numerical computations on data that may be presented in the form of arrays or matrices.
SciPy is a free software library for doing technical data processing as well as scientific data processing.
Scikit Learn is a free machine-learning software library that includes numerous clustering and regression, and classification techniques. Besides, Scikit-learn can be combined with SciPy and NumPy and used in Python programming.
Python language has a high compiler speed
Python is best used with Big Data development services because of its quick data processing speed. Python programs are written using simple and manageable code, and hence they can be executed faster when compared to other programming languages.
Python is extensible and portable
Python’s portability and extensibility make it a great choice for a variety of cross-language tasks. Python is compatible with a wide range of operating systems, including Windows, Linux, Mac OS X, Solaris, etc. as such, Python can also be utilized with Java, C/C++ libraries, or .NET components.
Python has support for data processing
Python has built-in support for data processing. There are available tools in Python programming language that can be utilized for recognizing as well as handling unstructured data like speech, text, and images.
Python can also process data spread across multiple files like HTML, XML, SQL, CSV, and JSON. Pandas, SciPy, NumPy, and other Python libraries can also be used for data processing.
Python programming language has enhanced compatibility with Hadoop
Python is securely compatible with Hadoop, and in this context, it is relevant to say that both Hadoop as also Python are open-source Big Data systems. Since there is a range of Python libraries used for data analytics and so most developers prefer using Python along with Hadoop rather than Java or Scala.
Python gets support from a large community
Many people globally use the Python programming language, and therefore it is to say that the Python community is large.
Python programming language provides support for data visualization
There are a large number of packages that are associated with Python, and these packages, like Matplotit, NetworkX, Plotly, Pyga, Seaborn, ggplot, Altair, etc., can be utilized for data visualization.
Python utilizes IDEs for data sciences
Python offers several integrated development environments (IDEs) that facilitate data visualization, machine learning, data analysis, natural language processing, etc. As such, the programming language is very much suitable for data science. The IDEs associated with Python are Spyder, Rodeo, and Pycharm.
The content write-up focuses on why Python is a suitable language for Big Data development. From the write-up, it is evident that several factors make the Python language popular and most sought-after for Big data development services. To know more in this regard, visit relevant resources that are available on the web.
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