- August 31
- 3 min
There are many misconceptions around Python, actively stopping enterprises from choosing this language for their application development projects. Of course, Python is not flawless (which one of the programming languages is?), but it has many perks that make it a great choice for building stable software in a very fast way. We will look into this perk and discuss the benefits they can bring to your project.
Table of content:
Django – a star of web development
Django is advertised as “the framework for perfectionists with deadlines”, and this slogan is incredibly accurate. Django speeds up the development process. How? By giving developers many features out-of-the-box, ready to use without any additional configuration. One of such features is the admin interface that you get by default when starting a project. It provides you with a tool for managing the content of your site and lets you skip a few steps at the very beginning of work.
Another example is the user authorization feature. By default, when you create an app in Django, you have the user authorization module enabled. You can manage users, their permissions, groups, and so on. This means you do not have to implement it yourself and spend a week writing and testing the ACL system and so on. This and similar features let you focus on the core of the application, instead of writing boiler-plate code.
A cherry on top is a huge community. That means thousands of modules that expand the framework, making developers work even quicker and more efficient.
Programming with Django and Python is very fast because you are not wasting time on small things. It is also cheaper because we get a lot of features for free. There are also a lot of modules extending the framework itself. Overall, Django is a proven framework, used by huge organisations. For example, Instagram is powered by Django.
Are we sacrificing performance for speed?
If you want a reliable answer, you should ask those questions to companies such as Spotify, Instagram, Udemy, Uber, or even Google. All of them own world-class software written in Python. So as you can see, the language’s speed is not a problem here.
Yes, Python is not the fastest. And putting on it a huge layer of abstractions in a form of Django does not help. While Django is challenging in terms of performance in very large applications, it also offers a lot of room for optimization. The net effect is that you quickly build an application, quickly get into the point of MVP, and then have wide possibilities to improve its performance. If necessary, Django gives you optimization paths.
It comes to the need of a client and their priorities.
- If a client wants to have a product as fast as possible, for example, an MVP in less than half a year, you pick a framework like Django, choose expanding packages and libraries and develop the software in a very short time.
- If a client is focused more on performance and scalability, you pick an asynchronous web framework, or even decide on microservices architecture, then prepare a database foundation for future data science use, and generally develop a stable, well-rounded software.
The key here is to understand that Python is flexible and allows various approaches. At our custom software development company, we have the competencies to help you choose the best way to a successful project.
If a client wants to benefit from the cloud’s advantages, the projects can go in one of three ways.
- The simplest way, recommended for a smaller application, is to simply write it in Django, and then deploy it in Elastic Beanstalk service in AWS or App Engine in Google Cloud Platform. It will deploy itself on an EC2 machine, and RDS relational database. Processes such as scaling or load-balancing are automatic.
- Another approach is to go cloud-native. Since Python is supported by all cloud suppliers’, you can easily write lambdas and cloud functions for AWS or Google Cloud Platform. There is a wide range of libraries to use, for example, Google’s SDK. It is not a library written by two friends in their garage – such solutions go through all QA tests and has very high quality.
- The last approach has little to do with the language itself. You can base your project on Kubernetes. If you build your applications container’s image, it is AWS, Azure or Google Cloud Platform’s task to run and scale it according to users needs. You can do this with Python without any problems.
Your project in Python can take many forms, there are tons of things you can do. And since many PaaS services natively support this language, you can choose whatever suits your needs. We have competencies in AWS, Azure and Google Cloud Platform, so if you want to discuss which cloud provider will be the best for your enterprise project, just contact us!
New type annotations have been added. At the stage of writing the code, you can run static analysis, something like a will check for example if the arguments of the function have the correct types or do the functions return data of the correct type. This and newer language features make it possible to better verify the code before we push it to production.
What is more, the culture of testing in python is very strong. Since the language is dynamic, the only defence against bugs – or a way to be sure the code works – are tests. This culture, test writing, and test tools are all very strong points in Python. And annotations add the next level of verification.
When you invest in a big project and have an enterprise application that will be developed for your company (or users) for years, this application will always have some additional baggage. For example, for Java projects, it will be many levels of abstraction. For Python, it will be a billion tests resulting from the fact that the language is dynamic.
Our client’s case – Python in an e-commerce platform
The project consists of three parts: e-commerce platform, products catalogue, and back-office with integration between many different shops, partners and suppliers. As for the product and architecture, up to 90% is based on Django. There are some supplementary services, like elastic search or redis for caching. It is deployed on Kubernetes and runs on Google Cloud Platform.
An amazing thing about this project is that it started as a quickly developed MVP. Since then, the code is not only maintained but also developed: new features are constantly added.
Sometimes clients are worried about investing in the new project because they are not sure if the enterprise application can be integrated with other solutions, for example, accounting applications or warehouses platforms. That problem simply does not exist with Python. You can integrate your software with anything that has a web API. What is more, companies that take care of the programming interface of their products often offer SDKs of such quality, that the basic integration can be done in one day.
Will your Python application stand the test of time?
Python has a great community creating new libraries and extensions, but more importantly – it has the foundation that develops it wisely. Thanks to this, it is certain that its frameworks will be further developed and will not disappear overnight when their creators run out of ideas.
Bug fixes are introduced quickly, even up to several versions back. Even if we have not done upgrades for a year, we still have access to releases. There is no situation when it is too difficult to upgrade because too many things have changed.
TIOBE index indicates, among other factors, which language brings the most satisfaction to the developers who work with it. In the index Python scores high both in terms of the number of people working with it and in terms of the quality of their work. It is a programmer-friendly language, and you could say: happy programmer means better software.
We are talking here about a stable framework where both adding and subtracting features is very much discussed and very closely guarded, so changes do not spoil anything. This is great stability.
Another thing is data science. If a company plans to gather and work on large amounts of data, then investing in Python is a good idea. Why? Because next to the programming language R, Python is the most popular in data science. Programmers working with the application can, after a short training, move to the analytical department and write data models there. It gives you a chance to have a better flow of people in the company.
We can support people who write scripts related to data science. If a researcher creates a model in Python, we can help them and write it in a way that makes it work much faster and more efficient. We can create a working production code in longer than one, two weeks. It is simple, effective, and time-saving.
We hope you see now all the perks that using Python in enterprise web projects can bring. With a language that is stable, has many high-quality extensions, lets programmers work very fast, and is supported by virtually anything, you are simply bound to succeed.
We at the DSS Hicron team can help you with many areas of your Python project. To name a few possibilities, we can:
- help you choose the best approach to the web application’s architecture,
- quickly write an MVP product,
- develop an enterprise app for the cloud,
- support your data scientists in writing better models,
- or deliver software that will let you achieve your business goals for many years to come.
If you are wondering which programming language is the best for your next enterprise project, hopefully, this article put Python in a good light. If you have questions or wish we help you in choosing the best approach – write to us through the contact form below!
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With HICRON’s creative ideas and fresh perspective, we reached a new level of our core platform and achieved our business goals.
Many thanks for what you did so far; we are looking forward to more in future!
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