6 Python Myths That Hold Back Enterprise Adoption, Debunked

For enterprise applications Java and .NET have been industry favorites. Though some of the most popular and widely used websites, such as Google, YouTube, Dropbox, Reddit, Pinterest, Instagram, Quora, Spotify, etc. use Python. The reason behind the adoption of Python for enterprise solutions has been ‘reliability’.

Enterprise software needs the flexibility to integrate with legacy and authentication systems, often needs customization to fit operating models, and involves many stakeholders that contribute to the development process. Thus, the development process of enterprise software becomes a lot more complicated than regular software development and hence demands ‘enterprise-grade’ programming languages. While in its initial days Python did not meet the infrastructure demands of enterprise-grade software, since the 2000s, the Python community has worked relentlessly to make its open-source libraries for web development frameworks and data analysis enterprise-ready. Despite the large scale use of Python and rising demand for Python development services, here are 6 myths still making the rounds that impede Python enterprise development adoption:

Common Myths About Python for Enterprise Development 

Myth 1: Python Is Only a Scripting Language 

While Python can be used as a scripting language since it has a simple syntax and cross-platform support, it has to be noted that Python essentially is a general purpose ‘programming language’ that is flexible enough for full-scale Python app development and can also be used for scripting. This easy-to-use use language works as both a programming language and a scripting language making it suitable for enterprise use cases.

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Myth 2: Python Is Not Compiled

One of the myths about Python enterprise development is that unlike Java and other such languages, it is not compiled. Contrary to this belief, Python compiles code automatically and does not need a separate compiler toolchain, as it compiles to bytecode, which a virtual machine runs, an important capability for Python development services handling enterprise workloads.

Myth 3: Python Does Not Scale 

This is one of the biggest myths surrounding Python for enterprise adoption. If Python did not scale then why would companies like Dropbox, YouTube, Instagram, etc. with a massive user base be using it? Python lends itself easily to horizontal and vertical scalability, making it ideal for Python enterprise development projects. In terms of the code development time, Python scales better since you do not waste time fixing segmentation faults and memory leaks or writing makefiles for compilation. Python does not have a performance ceiling, but each Python runtime has its own performance capabilities that can impede or accelerate scaling depending on where it is used. At the same time, it should be pointed out that scalability issues stem more from the use of the wrong architecture than from limitations of Python itself.

Myth 4: Python Is Slow 

Python is also perceived to be very slow. However, in some cases while it might be a tad slower than C and C++, its speed is considerably faster than languages such as JavaScript, Ruby, etc. Each Python runtime has its own performance characteristics. For example, PyPy’s JIT compilation attains ‘faster-than-C’ performance. Since Python is a high-level and dynamic language, it does not demand coders to focus on low-level implementation or premature optimization making it easy for developers to make efficient use of their development time.

Myth 5: Python Does Not Support Concurrency 

Python is also perceived to be very slow. However, in some cases, while it might be a tad slower than C and C++, its performance is competitive for most Python enterprise development scenarios and faster than languages such as JavaScript, Ruby, etc. Each of the Python runtimes has their specific performance characteristics and is no slower than the others. For example, PyPy’s JIT compilation attains ‘faster-than-C’ performance. Since Python is a high-level and dynamic language, it does not demand coders to focus on low-level implementation or premature optimization making it easy for developers to make efficient use of their development time.

Myth 6: Python Is Not Suitable for Large Projects 

Python has been used successfully for large projects and big teams. Bank of America uses 10 million lines of Python code in a single project. YouTube, Instagram, JPMorgan are other examples of large companies using Python extremely successfully for large projects. It has exceptional packaging characteristics and modularity and most of the scaling guidelines remain the same making it great for large projects. Additionally, it has strong conventions, tooling and code review practices that make large codebases easier to manage. Teams can also run static analysis on Python code before it’s checked in.

Hiring Python Developers for Enterprise Projects 

Good Python programmers are relatively lesser than PHP or maybe Java developers. While developing your enterprise application with Python, ensure that you hire experienced programmers who not only have good knowledge of the language but also have an in-depth understanding of the basics of programming. 

Is Python Ready for Your Enterprise? 

In every myth surrounding Python, there lies hidden an implied strength. We hope this blog has helped in dispelling these prevalent Python myths and provided a new perspective on python for enterprise adoption.