Among programming languages, Python consistently emerges as one of the leaders in terms of popularity and use.
However, its role is much less prominent in one significant area: enterprise business applications. Why is it that a language so popular is virtually omitted from such a massive part of the market?
Well, it’s not so much about whether Python is popular (it is) or powerful (undeniably) but whether it aligns with the high-stakes demands of enterprise development. And the answer? As goes the consulting staple: It depends.
Understanding the Foundations of Custom Enterprise Applications
Enterprise software is a massive topic in itself. Pretty much every business has its configuration of systems in use – some in the SaaS model, some custom-built. I’ll focus on the latter type here.
What do I mean by a custom enterprise application? I’m talking about systems focused on business logic. This is where we apply Domain-Driven Design (DDD). DDD is usually linked to .NET and Java, not Python.
Of course, it doesn’t mean it can’t be done – there are implementations of DDD in Python and JavaScript, for example – but they are about 10-15 years behind the mainstream. This delay contributes to the perception and practice of large systems not being built in Python – most developments are continuations of existing projects.
Why Not Use Python for Line-Of-Business Applications?
First of all, we’re talking about a dynamically typed language, meaning more problems you find in runtime instead of compilation time. In smaller projects, Python’s flexibility with variable types is a plus. However, in large-scale applications, loose typing can lead to errors and make it harder to maintain consistent code quality. Tools like type hints and static type checkers (e.g., mypy) have been introduced to mitigate these issues, but they are not a substitute for strong typing if it’s needed.
Performance can be another tricky point. Python is an interpreted language, which means it’s generally less efficient than compiled ones like Java or C++. If you’re dealing with high-performance needs, speed matters. While numpy takes advantage of C language optimizations, numby is used for memory data processing and aggregation tasks, which are not the core of backend systems.
Scalability can also be an issue. Python’s memory management and concurrency limitations, especially the infamous Global Interpreter Lock (GIL), can cause bottlenecks in multi-threaded applications. This can be a concern in enterprise settings where performance at scale is critical. However, it’s important to note that Python offers alternative concurrency models like multiprocessing and asynchronous programming that can effectively address scalability issues.
Enterprises also look for strong tools for languages equipped with code quality and project management tools. Some languages like Java and C# offer built-in features that help track performance, enforce coding standards, and manage large projects more effectively. Basically, these are things that businesses rely on for long-term success.
Taken together, these reasons mean that large corporations and enterprises don’t typically build their backend services or solutions in Python. That said, enterprises can still successfully use Python to process data their business applications generate.
How Enterprises Can Benefit From Using Python
Long story short: Python is an unbeatable choice in the realm of data.
Data science and analytics
Whether you’re analyzing customer trends or processing massive datasets, Python delivers. It enables data science teams to make a significant impact when scaling business intelligence, be it through ETL pipelines or predictive modeling.
Artificial Intelligence and Machine Learning
Python is the backbone of AI and machine learning initiatives. Thanks to libraries like TensorFlow and PyTorch, businesses can experiment with automation, fraud detection, and predictive analytics. The challenge? Moving beyond pilot projects to full-scale deployment. But with the right strategy, Python is a powerful asset in driving AI innovation.
Scripting and automation
Python stands out when it comes to automating repetitive tasks, orchestrating workflows, or integrating disparate systems. It eliminates a lot of manual effort and helps businesses get things done faster with DevOps tools like Infrastructure-as-Code or CI/CD pipelines.
Discover New Possibilities with Python
Python is a powerful tool for businesses at all scales. The key is to match this tool to the task. If you’re looking to increase your organization’s data capabilities, Python is where it’s at.
Working with an experienced development company like Scalo can help when it comes to using business data at scale. Our Python developers understand the unique challenges companies face and can support you in building efficient, scalable, and tailored solutions to your business goals. Reach out to us today, and we’ll help you make the most of your data.
Radek Białowąs
Senior Python Developer
Radek is a machine learning expert who combines AWS cloud expertise with practical software development to solve complex data challenges. Through his articles, he shares insights and tutorials on Python, data processing, and ML implementation, making technical concepts accessible to others.
