8 Reasons Python & Big Data Are A Perfect Match in Digital Heaven For Web Developers

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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.

1 — Simple and straightforward interface

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.

2 — Open source

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.

3 — A range of libraries

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.

4 — Hadoop integration

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.

5 — Speed and performance

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.

6 — Portability

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.

7 — Security by default

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.

8 — Community support

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.

The bottom line

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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