Python Role in Big Data

What is Big Data?

The term “Big Data” refers to the data in bulk amounts with significant variations. The data scientists describe it in three Vs: variety, volume, and velocity. Overall, the data is so large and complex that the traditional data software cannot process it. However, this data is essential and beneficial in understanding modern business and economic problems.

Python’s Role in Big Data

Python provides strong support for various kinds of data with its powerful packages and libraries, such as image and sound. Moreover, social media data analysis requires unstructured and untraditional data processing with vast and complex data patterns. Therefore, python has in-built features to support complex and large amounts of data, making big data and python beneficial for each other.

Benefits of using Python in Big Data

Following are the benefits of using Python language for Big Data processing:

1. Easy to Learn

It is a user-friendly programming language that abstracts various complex things with its in-built methods. Therefore, the users can quickly learn it and have to code fewer lines to execute the program compared to other programming languages. Moreover, in addition to faster processing, it provides features like auto identification, data types association, simple syntax, and code readability, making it easier for novice programmers to learn and do their tasks quickly. Moreover, the Big Data users can primarily focus on the data insights without going into the code’s technical depths, making it the primary reason to choose python.

2. Scalability

When users deal with a bulk amount of data, which is common nowadays with more data available, scalability becomes a significant issue. Python is relatively faster compared to other programming languages but has improved its performance with the release of Anaconda, making it more flexible for big data.

3. Open-source

Python, being an open-source programming language, has immense community support. Additionally, it is free and supports multiple platforms and operating systems such as Windows and Linux.

4. Powerful packages

Python has robust packages for big data’s data science and analytical needs. Following are a few packages that the users can utilize in their programs and software relating to extensive data analysis and exploration.

1. NumPy

This package allows the users to store and manage a large amount of data. Moreover, it makes it possible to perform scientific computing on complex data such as images with multi-dimensional arrays and advanced mathematical methods.

2. Pandas

This package helps the users in data manipulation and analysis. Moreover, it can give structure to the unstructured data because most of the data used in big data are unstructured and bulky, and structuring it can be tiresome.

3. SciPy

This package helps the users in technical and scientific computing. It has various modules and methods to perform calculus and linear algebra functions such as integration, interpolation, and Fourier transforms. Moreover, the users can also use this package for image and signal processing and data optimization.

4. Matplotlib

This package helps in data visualization. It is a multi-platform package library built on NumPy arrays. It provides an interactive user interface to visualize the required data in bar charts, scatter plots, histograms, and other graphical plot forms.

5. Portable

Python is portable, making it possible to perform various cross-language operations. Therefore, the users prefer it over another language to use their ML models with Graphical Processing Units (GPUs).

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