Using Python for Big Data – An Emerging Trend
Big Data – these two words came together to define the data revolution. Obviously with organizations turning toward customers as their primary source of data and analytics, there was bound to be an increase in data processing and structuring. The flow of data coming in has drastically increased and the amalgam of two words ‘big’ and ‘data’ perfectly sums up exactly what it means for businesses in the analytics industry now.
What first started as a buzzword, or a buzz phrase as some might call it, has now become a talking point in the tech industry. Industry experts have set their eyes on big data and are waiting for the tides to lead them into future trends.
Looking into the hazy view of the future in our crystal ball, one can predict the following trends for Big Data in the coming year or even years:
- Data analysis and data synthesis come together for the emergence of data competence in analytics
- Big data shifts to form a new facet of ‘wide data’ through the amalgam of disparate data sets
- Self-service analytics for consumers on the market
- Improved speech processing and recognition software operations to improve interaction with customers
- Emergency of algorithms that implement analytical systems for the identification of data patterns and techniques
- Machine learning as a catalyst to improve intelligent data catalogs
- Big data in climatic research. Researchers in the field will heavily rely on it for analytics and patterns
- Real-time data analysis will become a crucial requirement within most sectors and industries
All of these trends host some serious potential for businesses in the future. But there is one more trend helping businesses leverage the potential of big data in their firms. This trend heavily relies on the programming language, Python.
[ Also Read: Role of Python in AI ]
Yes, you read that right. The programming language Python, which was previously used for web development and web apps, has now become a darling for big data. But what is it that makes Python so impressive for Big Data? Let’s take a look:
Easy to Use
First off, Python is one of the simplest languages to not only learn as a training programmer, but to also use. For this very reason, you will find that the barrier to using python is quite low. In simpler words, if your business is looking to leverage the potential of big data through Python, your programming team can be up to date with Python without having to take an inordinate amount of time.
Python is easier to use because it uses simple English commands rather than relying on codes and complex software engineering processes for stuff to work. With Python, you get to write and run a code of choice, which is why the language doesn’t require an additional compiler.
Python is known to be an open-source language. This means that codes within the platform are open for almost anyone to not only see, but also to change and use within their work. Why does this help with leveraging big data? Because a number of organizations today use open-source software in their systems to help boost their pipelines! Being open source also means that it is a lot easier for businesses to add and integrate the code within their current systems.
Seamless integration is a major requirement for Big Data processes, as NoSQL databases will have to be integrated within the operation. This becomes easy through Python’s open-source technology.
Wide Library for Big Data Operations
A wide library with operational capabilities suited for big data is another factor driving the trend toward Python and Big Data forward.
Important Python libraries that can work with Big Data include:
- NumPy is a Python library used to compute scientific commands. This library provides support for random number crunching, matrices, linear algebra, Fourier transforms and a number of other complex mathematical functions
- Panda is a data analysis library by Python. This library structures data and performs data manipulation on both, numerical tables and time series data
- SciPy is a Python library that contains modules for linear algebra, integration, optimization, interpolation, ODE solvers, signal and image processing and other engineering and scientific tasks
- NetworkX is a library used to study graphs within Python
- Theano is a library that runs numerical computations. It can also help evaluate expressions based on algebra and arithmetic
- Dmelt is used for statistical analysis and numeric computation of big data
Python is known to be compatible with Hadoop and is well supported in this regard. Hadoop is an extremely important Java framework that uses a cluster of programming systems to solve problems relying on massive collections of data.
Enterprises and corporations can employ Hadoop to create incredible numbers of data through commodity hardware and without purchasing servers. This framework can run large amounts of data and help save money.
Python enjoys compatibility with Hadoop Streaming, which makes it a lot easier to execute scripts and perform Big Data jobs.
Support for Processing of Image and Voice Data
Big data doesn’t just deal with characters and strings of data – especially not in the future. In fact, Big Data has started working with voice recordings and images. Python provides support to both, image and audio data, and helps solve complex structural problems.
Before your company jumps onto the Big Data bandwagon, remember to set your steam right by employing a team of Python developers. These developers will take your organization forward.