Python and Big Data, a current trend

June 1, 2024 Roberto Magalhães

If your company has not yet joined the Big Data movement, it is not too late. Just familiarize yourself with Python development.

Big data. Two words that tend to be quite divisive. Depending on which side of the fence you're on, you may be constantly on the lookout for exactly where Big Data is headed in the next year.

Looking into the crystal ball, there are some interesting Big Data trends to watch in the coming year:

Big data shifts to broad data by uniting disparate data sets.
Data synthesis and analysis come together to form data competency.
Self-service analytics offered to consumers.
Algorithms will be used to support analytical systems in identifying data patterns.
Improved speech processing for better interaction with users.
Machine learning will be used to create intelligent metadata catalogs.
Big data will be heavily used by climate researchers.
Real-time data analysis will become crucial for certain industries.

These are some truly important trends for the future, some of which could very well change the very basis of how companies operate. But there is another trend that helps companies make better use of big data. This trend involves Python .

That's right, the programming language used for web applications and web development in general has become the darling of big data. But why? What makes Python so good for Big Data? Let's take a look.

Easy to use

Firstly, Python is one of the easiest languages to learn and use. Because of this, you will find that the barrier to entry is quite low. In other words, your developer teams won't spend a lot of time getting familiar with a new language just so your company can take advantage of Big Data.

What makes Python so easy to use? Unlike many other programming languages, Python focuses on using the English language to create a simple, user-friendly syntax that does not require users to fully understand how software engineering works. It also helps that Python doesn't require a compiler. In fact, with Python, you write and run code.

Python is also compatible with almost every major platform on the market, meaning you can write Python code and scripts to and from virtually any device.

open code

Python is an open source language. What does that mean? Being open source means that the code is available for anyone to not only view, but also change and distribute. Why is this important for big data? The reason is the same as why so many enterprise users have adopted open source software to help boost their pipelines. Being open source means it is exponentially easier for companies to integrate with the software and systems they already use.

This is a key element of Big Data, as tools like NoSQL databases must be able to integrate seamlessly with other software. Since Python is open source, this is not only possible but also easy.

Vast library perfectly suited for Big Data

One of the biggest things driving the Python/Big Data trend is the sheer number of Python libraries that are perfectly suited for big data.

The most important Big Data-centric Python libraries include:

Pandas is a library created specifically for data analysis that provides the data structure operations necessary for manipulating data in time series and numeric tables.
NumPy is the scientific computing-specific library for Python, which provides support for linear algebra, random number processing, Fourier transforms, multidimensional arrays, matrices, and other high-level mathematical functions.
SciPy contains modules for optimization, linear algebra, integration, interpolation, FFT, signal and image processing, ODE solvers, and common scientific and engineering tasks.
Mlpy is a machine learning library that works on top of NumPy and SciPy to provide the ability to find a compromise between modularity, reproducibility, maintainability, usability and efficiency.
Matplotlib adds support for 2D plotting and print publication formats and generation of plots, graphs, histograms, error plots, power spectra, and scatterplots.
Theano is a library for numerical computation, it allows you to optimize, define and evaluate mathematical expressions.
NetworkX is a library used to study graphs.
SymPy makes it possible to add symbolic computation with basic symbolic arithmetic, calculus, algebra, discrete mathematics, quantum physics, and Dask (an open source library for parallel computing).
Dmelt is used for numerical calculation and statistical analysis of big data.
Scikit-learn is another machine learning library that includes regression, clustering algorithms, and TensorFlow.

Support for image and voice data processing

Big Data is not just about numbers and strings of characters – especially in the future. In the coming years, Big Data will have to work with images and voice recordings. Consider how many consumers are using Google Assistant, Siri and Alexa. Although these commands are not saved on the respective servers, they need to be executed in real time.

Thanks to support for images and data (through several libraries), Python is an excellent solution for solving these very complex problems.

Compatible with Hadoop

Python is well supported and compatible with Hadoop. Why does this matter? Because Hadoop is a very important Java open source utility framework that facilitates the use of a cluster of computers to solve problems that depend on massive collections of data (also known as Big Data).

By employing Hadoop, companies can make use of commodity hardware (instead of having to buy expensive servers) to create massive clusters to handle incredibly large amounts of data, thus saving significant amounts of money.

Python allows you to work with Hadoop Streaming, which makes it easy to create and run Map/Reduce jobs with any executable or script as a mapper and/or reducer. This is a very important task for your Big Data jobs and made easy with Python.

Conclusion

If your company has not yet joined the Big Data movement, it is not too late. But before starting this important journey, make sure you have a team of Python developers ready. With these engineers on hand, your company can leverage Big Data in ways it otherwise wouldn't be able to.

If you liked this article, check out one of our other Python articles.