How to Leverage the Power of Python for Processing Big Data

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Jakub Protasiewicz

Dec 12, 2018 • 6 min read

Python is one of the hottest languages on the tech scene right now. Why?

Well, it’s easy to learn, code in, and apply to different problems. No wonder that developers and businesses alike love it. At the same time, a technological breakthrough is changing the world of software and business – the ability to process huge amounts of data and glean useful insights from it. What do Python and big data have to do with each other? Well, the former happens to be the perfect tool to process the latter. How so? Read on to find out!

Why You Should Choose Python For Big Data - Free Tools

Python is popular partly due to the immense number of ready-to-deploy libraries and frameworks available. This holds true also for tools related to big data processing. Let’s have a look at some of the available libraries.

  • NumPy: a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records;

  • Pandas: a library used for data manipulation and analysis, numerical tables and time series in particular;

  • SciPy: a library used for scientific computing and technical computing, with modules for optimization, linear algebra, integration, interpolation, image processing, and much more;

  • Scikit-learn: a powerful data processing package with built-in support for such operations as classification, regression, clustering, preprocessing, model selection, and dimensionality reduction;

  • Pybrain: a machine learning library known for its modularity and ease of use;

  • Tensorflow: a Google-made “machine learning framework for everyone” with great support for high-performance numerical computation.

The best thing about all of the above? They’re entirely free to use. No need to sign a contract or deal with licensing – just download and go.

Python is easy to integrate with MapReduce

First developed as a proprietary Google technology, MapReduce is now a commonplace term in the world of big data. At a very high level and in very plain English, the whole idea is about making huge data sets smaller and thus easier to process. While we don’t have the room to go into too much detail here, the important thing to know is that using MapReduce enables you to parallelize (distribute across multiple computers) data processing workloads.

Two of the best MapReduce tools, Hadoop and Spark (both made by Apache) happen to have great support for Python. This means that there’s no need to spend precious developer time glueing together your Python codebase and Apache’s offerings – they come with batteries included.

You can use Python to explore data with Jupyter Notebooks

The Jupyter Notebook is an application that allows you to very easily do things like data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning – all using Python code. Jupyter Notebook also allows for adding pictures, narrative text and so on, making it a dream come true for both big data crunchers and the business people who read their reports. What’s more, Jupyter offers the application for free, so you can try it out (or use it for business purposes) without incurring any additional expenses.

There are great data visualization tools for Python

They say that seeing is believing, and we agree. After all, how much would data be worth if you couldn’t use it to tell a story? It’s a good thing then that Python offers a number of great visualization tools for any purpose. Some of our favorites include:

  • Plotly: an enterprise-grade framework for building data analytics web apps, such as dashboards;

  • Matplotlib: a great mathematical plotting library integrated with NumPy;

  • ggplot: a library that can help you build professional plots in as little as five lines of code;

  • Pygal: which is best described by its tagline – “sexy Python charting”,

  • NetworkX: a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Remember how all of the tools we mentioned above were free? Well, so are these (with the exception of Plotly).

Python has an outstanding community

Thanks to its popularity and versatility, Python has amassed a huge following of developers, academics, analysts, and everyone in between. This means that if you encounter a problem with Python, it’s easy to find the answer – or someone who knows it. Community support also means that vulnerabilities and bugs are patched quickly, and new frameworks and features are released frequently. Yet another benefit is the fact that recruiting Python developers is much easier than finding ones familiar with other languages or technologies.

Wrapping up

If you’re planning to start leveraging the data your business generates to go to the next level, Python is probably your best choice. With its combination of relative simplicity, the abundance of free visualization and analysis tools, and outstanding community support, you really can’t go wrong if you choose it for your next project. Are you intrigued? Make sure to get in touch with us – our in-house team of Python experts will be happy to answer any question you throw at them.

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Jakub Protasiewicz

Engineering Manager

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