Chief Executive Officer
There is no dispute that the popularity of Python continues to rise. Last year Python had pipped Java to take the ‘Number Two’ spot in the most popular programming languages. The IEEE placed Python at the top of the list based on the suitability factor. But, is the Python hype really worth it?
What are the factors that are important for ranking programming languages? Short and easy-to-read code and measuring code being run in production such as in addictive mobile games can perhaps be used as deciding factors. However, there is no easy metric to decide the same.
At one time Python was considered a low-level scripting language unlike the present-day open-source status that it sports. Enterprise managers are wondering if Python is worth the hype that it is creating. Python wins rave reviews for its end-user benefits than for complex enterprise apps. Why these mixed opinions? Let us figure out what are the pros and cons.
Python is among the most popular open-source languages. Tools and libraries are aplenty and the army of qualified programmers in Python is growing by leaps and bounds. Many institutions have included Python in their itinerary for inducting newbie programmers. All these make Python a much sought-after language indeed. Programmers always like to write the latest technology using the most popular language.
These differences are like throwing the spanner in the maintenance work. The new-generation coders are not used to reading code in the older languages. They end up writing ‘glue code’ to translate this data into an understandable format. This causes the amount of code to grow without actually accomplishing much.
The code written to just maintain a ledger may have a million lines of code thereby increasing the overheads to very high levels. The whole maintenance exercise becomes a daunting one. Data conversion takes place many times before being submitted in the correct format for the business logic code of yesteryears to work on it.
People often wanted to use simple languages to solve little and simple problems. It was best to solve these without resorting to the usual software paraphernalia: loading the IDE and suitable libraries, creating code repositories, configuring CI/CD path, etc. Instead, if they could write small snippets of code that did the job without a flaw, the coders would prefer that. This is exactly where Python steps in. They believe that the same simplicity could be extended to as many lines of code as enterprise applications require.
Spreadsheets can be programmed to accomplish humungous jobs without a flaw. Many a time coders cannot accept the fact that spreadsheet macros can accomplish just as much as a complicated piece of code. This is the very reason why many of the enterprise tools use spreadsheet reports. There is no need for you to sink your teeth into Python code when there is a spreadsheet cake already available which you can swallow easily!
Without a need for the programmer to laboriously define data types, the coding can be quick. Moreover, the Python interpreter is designed to quickly flag errors during runtime making work easy for the programmers.
There have always been complaints about dynamic languages on how they can cause crashes at the most unexpected places. Debugging becomes a messy job in such a case when you try to spot the problem.
With typed languages, programmers try to add a few lines of code that provide information which makes it easier to spot problems when they occur. This makes it easy when large teams work together. Python programmers do not use explicit data types. However, now having learned some lessons, many have started to use data type ‘hinting’. This is helping to solve many of the Python debugging problems that were rampant.
Python is arraigned as the cause for the rise of the casual programmer. Many programmers can sort out complex tasks using Python’s basic-level programming tools. Despite this, some serious programmers still dismiss the capabilities of this low-level scripting language.
With serious tools being the call of the day, many enterprise application coders believe that any language that may be fir for writing 10 lines of code need not automatically become good for writing 10,000 or 1 million lines of code. Such coders are comfortable and rely on languages such as Java or C/ C++ to write enterprise application.
IT companies are into AI and machine learning and are using more complex mathematics today. Python, incidentally, has displayed its prowess in these areas. Your machine learning libraries could well be written using Python.
Enterprise coders are not carried away by fads. They are not willing to believe that fads will last a long time. Their impetus would be in finding a language where the bulk of the work gets done with just one language, not have different code-bits using different languages. This perfectly suits enterprise-type applications.
Python has the capability to take programmers for a joy ride on its libraries. These coders have gone on to create more libraries and this has paid off. Many coders would still choose Python over other options looking at the availability of these libraries and what they can help you accomplish. When you are solving a complex problem, you cannot discount help at hand in the form of extensive libraries.
Whereas Java is a language that has evolved slowly, there has been one too many variants of Python in a very short time interval. That is to say, each new version is typically a new language (Python 2.6 and Python 3.0). This non-uniform growth is what disturbs the enterprise coders.
Linux has been coded using C and assembly language, but Python has found a solid connection with many Linux distributions as part of different kernel versions. Pairing Python with Linux machines is greatly helpful because it forms the right fit.
The best fit between Python and Linux has its downside. The changes that came by between Python 3.6 and 3.7 caused the Ubuntu-Python fits to work erratically. Though Python has built many tools to manage these issues, coders feel that this would only add to the existing confusion. Different versions cannot be installed in one container. When the pile of code is very big, such as in enterprise applications, this kind of situation can cripple the smooth execution.
The open-source nature of Python is the very reason why there are so many innovations, libraries, and experimentation happening with the language. It is common for languages that have been created by corporations to have a single compiler. New versions are released from time to time with new capabilities. However, with Python, there are umpteen choices to keep your programs running. The numerous compiler choices exist are because of the experimentation that is possible.
Enterprise coders are sceptical about choosing their programming language from an array of options/language versions. Moreover, coders do not have the time to test and determine which version is the best. Many a time this is a downside of open-source language environments.
Now that I have stated the pros and cons of using Python for enterprise software applications development, our experiences tell a different story.
s the technology head for a custom software development company with a core focus on Python services, my preference for the language may give you the feeling that I am partial to it. Well, this wasn't the case till a few years ago when we used Python for our off-shore development projects. It made sense for us because we were a newbie technology company that chose this not-so-favorite language for finishing off small projects.
From then on, we have tried our luck with various other programming languages like C++, PHP, Perl, Golang, and so on. And Golang also remains one of our favorites. As of today, we have delivered over 300+ projects using Python to clients all over the globe. We have used Python to power the majority of our clients’ applications. From the wake of our experience, Python is an excellent choice for top-notch enterprise applications.
Python can be used to develop high-performing enterprise apps. Python can be used to build apps that smoothly adapt to changing business scenarios. It may not be a perfect language, and it does have certain limitations. Even its Global Interpreter Lock (GIL) is improving with each new version.
Every language is secure if the programs are written and executed in the right manner. Using a programming language, you can write secure code or otherwise. It all depends upon your coding efficiency. That the tech giant Google uses a lot of Python code lends credence to the fact that the language can be indeed used for creating and using secure enterprise applications.
We have answers supporting Python for scalability and access to data issues as well. Consider Instagram, which has over 1 billion users worldwide. Why did Instagram choose Python for its application? The answer is because it is simple and practical. There are other examples such as Youtube, Dropbox, Paypal, etc., all of which run on Python. This is enough evidence that Python is not just for building small-scale apps but can also be used for large enterprise applications.
In the end, it isn’t just about the popularity of a programming language, but it is surely a significant contributing factor. As the number of Python users increase, the community continues to expand its horizon. With this, guaranteed community support is available for your Python applications. In these cases, a lack of skills is one of the last issues you would ever encounter.
I would say Python is a battle-tested server-side programming language. The fact is that Python is fast, scalable, and productive and hence, Python becomes a candidate to code your enterprise applications. It is therefore on par with other programming languages in terms of enterprise software applications development and, in some cases, even beyond.
Enterprise software development brings with it a whole lot of challenges. Whatever be the complexity of the application, it requires strong domain expertise, vision, and extensive experience to pull it off.
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