Every developer, data scientist, and amateur coder has their favorite language when it comes to working with big data. Working with vast data sets necessitates that a programming language be easy to use, but also fast enough to provide accurate business intelligence within reasonable timeframes.
We’ve already written about the advantages of Python for data analytics, and shown you a couple of examples of this, such as how to write a headless web scraping bot in Python. Some developers, however, felt that our choice was a strange one – why use Python, they asked, when there are plenty of other languages around?
Well, in this article we’ll answer that question by taking you through the eight primary advantages of Python for working with big data.
First and foremost, Python offers an interface – whether through an IDE or working raw code – that is easy to understand and difficult to make mistakes in. This is a real benefit in a world where poor data quality costs businesses between $9.7 to $14.2 million each year worldwide.
It might be that other languages offer faster performance, or better web integration, but when it comes down to avoiding costly mistakes, Python is going to outperform them.
Equally as important is the fact that Python is an open-source language. This means that it has been produced and developed by a community of coders, scientists, and developers, who together understand the language at a fundamental level.
This, in turn, offers several advantages when using Python to work with big data. One is that testing your applications for security issues is much easier. Both dynamic and static application security testing, for example, have access to your code at a deep level and can scan for vulnerabilities while your applications are being run.
One of the major reasons why Python has become one of the most popular programming languages around is the range of libraries available for the language. Like Java before it, Python has been built in a modular fashion, in which the base language can be adapted and customized depending on the needs of the end user.
When it comes to working with big data, the popularity of Python has led the community to develop several libraries that are specifically designed to allow you to work with large datasets, and to optimize the computations you are performing on them.
Of particular note in this regard is Pandas, a free software library used to manipulate data and to re-encode data so they can be used across multiple systems, and Numpy, which has been built to extend Python so it can be used to compute in arrays and multidimensional matrices.
As we have pointed out before, it’s no good building a big data system that is unable to communicate outside of its own limited sphere. Instead, big data developers should focus on ensuring compatibility between their systems and those used by the organizations who will ultimately be using them.
In most environments, that means making your big data code compatible with Hadoop. Via the PyDoop package, Python is highly compatible with Hadoop, and provides a number of high-level tools for working with it. These include direct access to the HDFS API, so you can work in Hadoop from Python, and the MapReduce API, which is able to refactor computationally expensive problems into simpler ones.
Did you know that less than 1% all data produced is actually analyzed. There is a good reason for that – while our ability to collect and store data has improved exponentially in the last decade, our ability to process it has lagged behind.
This means that, in many big data systems, the limiting factor is processing speed. Python, which executes extremely quickly due to a simple syntax and relatively straightforward memory management, is therefore a great choice for developers looking to get the most out of their hardware.
The portability of a programming language is crucial when developing big data applications, because of the range of environments in which these applications are now deployed, and the range of levels of expertise possessed by end users.
The global Big Data healthcare analytics market was worth over $14.7 billion in 2019, for instance, but the level of coding knowledge in the industry remains relatively low. Developers must therefore make sure that their applications and systems work on a wide range of third-party hardware and software platforms.
This is yet another reason why Python has become very popular in recent years – having been developed as an inherently multi-platform language, it is easy to port code across systems, and easy to optimize it to run on different hardware infrastructure.
This multi-platform agility is particularly prized in the big data space, due to the fact that many data scientists prefer to to work via graphical interfaces, particularly when they are working with machine learning tools. Python provides easy, intuitive support for these models, and ensures that data can be passed between teams in a compatible way.
For many people, security will not be the first thing that comes to mind when they think about Python. The language suffers from a problematic perception that it is insecure, due in large part to advertising campaigns that have stressed the cybersecurity credentials of its rivals.
In reality, however, ensuring genuine security means going back to the basics when developing big data applications. And since Python is easy to use, and easy to understand, developers are far less likely to introduce vulnerabilities into applications written in the program, and are therefore far more likely to write secure code by default.
The security of Python is also boosted by the level of community support offered for language, because amateur or cautious big data developers can easily seek guidance and support from this network.
Many of the advantages listed above stem from the simple fact that Python has a large, knowledgeable, and enthusiastic community behind it. In fact, there are few languages that have inspired such a following, even within the open-source community.
In practice, this means that if you are struggling to find a solution to a big data problem, someone has come across this issue before, and already worked out how to solve it. Add to this the fact the top tech companies like Facebook, Instagram, and Netflix use Python in their products, and you’ve got a language that is eminently suited to big data projects.
Which language you use for a particular project will, of course, depend on the project and your personal preference. The advantages offered by Python, however, mean that it has quickly become the default choice for developers working in big data environments.
In fact, big data is now becoming so intertwined with Python that the communities are beginning to overlap, and what’s next for big data might depend, to a large degree, on the Python community. All the more reason, then, why you should begin to use it in your own big data projects.
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