Big Data. Two words that tend to be rather divisive. Depending on which side of the fence you’re on, you might well have your ear constantly to the ground, in order to know exactly where Big Data is heading in the coming year.
Looking into the crystal ball, there are a few interesting Big Data trends to watch out for in the coming year:
- Big data shifts to wide data by tying together disparate data sets.
- Data synthesis and data analysis come together to form data competence.
- Self-service analytics offered to consumers.
- Algorithms will be used to support analytical systems to identify 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 employed by climate researchers.
- Real-time data analysis will become crucial for certain sectors.
Those are some seriously key trends for the future, some of which might well change the very foundation of how businesses function. But there’s another trend, helping enterprise businesses to better leverage big data. That trend involves Python.
That’s right, the programming language used for web apps and general web development has become the darling of big data. But why? What makes Python so good for Big Data? Let’s take a look.
Ease of use
First off, Python is one of the easiest languages to learn and use. Because of this, you’ll find the barrier to entry to be quite low. In other words, your developer teams aren’t going to spend an inordinate amount of time getting up to speed with a new language, just so your business 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 doesn’t 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 the code.
Python is also supported on nearly every major platform on the market, which means you can write Python code and scripts from and for nearly any device.
Python is an open-source language. What does that mean? To be open-source means the code is available for anyone to not only see but change and distribute. Why is that important to big data? The reason is the same that so many enterprise users have adopted open-source software to help power their pipelines. Being open-source means it’s exponentially easier for businesses to integrate into the software and systems they already use.
That is a key element with Big Data, as tools like NoSQL databases must be able to be integrated seamlessly into other software. Due to Python being open-source, that’s not only possible, but it’s also easy.
Vast Library perfectly suited for Big Data
One of the biggest things driving the Python/Big Data trend is the vast 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 necessary data structure operations for data manipulation on both time series and numerical tables.
- NumPy is the scientific computing-specific library for Python, which provides support for linear algebra, random number crunching, Fourier transforms, multi-dimensional 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 both 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 hardcopy publication formats and generating plots, charts, histograms, error charts, power spectra, and scatter plots.
- Theano is a library for numerical computation, allows for optimizing and defining, and the evaluation of mathematical expressions.
- NetworkX is a library used for studying 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 numeric computation 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 isn’t just about numbers and character strings—especially not going forward. In the coming years, Big Data will have to work with images and voice recordings. Consider how many consumers are using the likes of Google Assistant, Siri, and Alexa. Although those commands aren’t saved on the respective servers, they do have to be acted on in real-time.
Thanks to support for both image and data (via a number of libraries), Python is an outstanding solution to solve these rather complex problems.
Compatible with Hadoop
Python is well supported and compatible with Hadoop. Why does that matter? Because Hadoop is a very important Java framework of open-source utilities that facilitates using a cluster of computers to solve problems that rely on massive collections of data (aka Big Data).
By employing Hadoop, enterprise companies can make use of commodity hardware (instead of having to purchase expensive servers) to create massive clusters to handle incredibly large amounts of data, thereby saving significant amounts of money.
Python allows you to work with Hadoop Streaming, which makes it easy to create and run Map/Reduce tasks with any executable or script as the mapper and/or the reducer. This is a very important task for your Big Data jobs, and one made easy with Python.
If your company hasn’t already jumped onto the Big Data bandwagon, it’s not too late. But before you start this important journey, make sure you have a crew of Python developers at the ready. With those engineers on hand, your business can leverage Big Data in ways you wouldn’t have otherwise been capable of.