As we all know, big data is very valuable in modern society. IDC says the amount of data it generates is growing fast and will reach 175 zabytes globally by 2025. A Megabyte is a trillion gigabytes. Now multiply that by 175. And then imagine how exploding the data is.

Choosing a programming language for a big data field is project-specific and depends on its goals. Whatever the goal of the project, Python is a big data programming language that is readable and capable of statistical analysis.

Python is a rapidly evolving programming language, and the combination of Python and Big Data is the developer’s choice because it reduces coding and provides powerful library support.

In this article, we explore the benefits of using Python in big data and the phenomenal growth rate in big data analysis.

Why is Python the right choice for big data? Simple coding

Compared to other programming languages, Python programming involves simple coding. We can execute programs with very few lines of code, and most importantly we can use Python to quickly correlate and identify data types. This language can process and express tasks in a very short time. Open source, easy to learn

Python is an open source programming language developed using a community-based model. It is free to use because it is open source, supports multiple platforms and can run in any environment (Linux, Windows, etc.).

Python is also easy to learn because its syntax is simple. This simple, readable syntax helps big data experts focus on managing big data rather than wasting time understanding the technical nuances of the language. This is one of the main reasons for choosing Python to handle big data. Statista points out that Python remains the most popular programming language in 2020, according to GitHub and Google Trends surveys, surpassing long-standing Java and Javascript in popularity.

Why is Python the right choice for big data? Python supports multiple libraries

Python is a well-known programming language, so Python is compatible with many libraries. These libraries help save time and make the language more popular.

Most Python libraries are useful for data analysis, visualization, numerical computation, and machine learning. Big data requires a lot of scientific computation and data analysis, and Python’s combination with big data makes them great partners. Python provides high compatibility with Hadoop.

Python and Hadoop are both open source big data platforms, which is why Python is more securely compatible with Hadoop than any other programming language.

Developers prefer to use Python and Hadoop because of its extensive library support. In addition, Python has the PyDoop package, which provides excellent support for Hadoop.

Let’s look at the benefits of using Pydoop packages:

Access the HDFS API - The HDFS API allows you to quickly read and write directory and file information without any barriers. Providing the MapReduce API - The PyDoop package provides the MapReduce API to solve complex problems with minimal effort. This API allows you to implement advanced data science concepts such as "record readers" and "counters," making Python ideal for big data.Copy the code

Why is Python the right choice for big data? Python is fast

Python’s high speed in data processing makes it best suited for big data. Python code takes a fraction of the execution time of other programming languages because of its simple syntax and manageable code. It supports a variety of prototyping ideas that make it faster to run code while maintaining good transparency between code and execution. This has always made Python one of the most popular options for big data in the technology industry. Python application scope

Python is an object-oriented language that supports high-level data structures. It allows the user to suggest data structures, including lists, collections, tuples, dictionaries, and so on.

It also supports various scientific computing operations, such as data frames, matrix operations, and so on. These incredible features of Python enhance the scope of the language to simplify and speed up data manipulation. This is the deadly combination of Python and big data.

Why is Python the right choice for big data? Python has data processing support

Python has a built-in feature that supports data processing of unconventional and unstructured data, which is the most common requirement for analyzing social media data with big data. This is why big data companies have chosen Python as an essential requirement for big data. Python is portable

This is the most important reason for Python’s popularity in data science. Due to Python’s portability and extensibility, many cross-language operations can be easily performed on Python. Many data scientists like to use graphics processing units in their machine learning models, and Python’s portability lends itself well to this.

Why is Python the right choice for big data? Python has a lot of community support

Big data analytics often tackle complex problems that require community support. Python has a large and active community of support, which helps data scientists and programmers gain expert support on coding related issues. In addition, enterprise support is an important part of Python’s success with big data. Top tech companies like Facebook, Instagram, Netflix, and others use Python in their products. Python scalability

Scalability is very important when working with data. Unlike other languages, Python is much faster. If the amount of data increases, Python can easily increase the speed of processing data, which is difficult to do in languages such as Java or R.

This allows Python and big data to mesh with each other with greater flexibility. conclusion

These are some of the most significant benefits of working with big data in Python. Big data technology is spreading across the globe, and meeting industry demand is undoubtedly a difficult task. But Python has become the perfect choice for big data because of the incredible benefits it offers. In summary, big data and Python together provide robust computing power on a big data analysis platform. I hope by now you have a good idea why Python is considered the perfect choice for big data.

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Author: Programming small jia