Nowadays, IT professionals are increasingly looking to DevOps and Python. If you want to know why Python is essential in DevOps, keep reading this article.
In the DevOps culture, we are required to do a lot of automation using tools and scripts, and Python provides many open libraries and modules to help with that automation.
If we want to write a script to automate a task, Python is an excellent choice regarding platform independence; it is easy to write and has integrations with many tools.
Also, many modules in Python are available as open source that support various tools. But before we go any further, see more about what DevOps is and what Python is.
What is DevOps?
DevOps comes from combining the words “development” and “operations.” It is based on the union of tools and a change of mindset to organize processes in a way that optimizes their results.
DevOps combines operations and software development that reduces or nearly eliminates the disconnect between the system administrators who run the infrastructure and the software developers who develop applications.
DevOps emerged from the need to speed up information technology deliveries, improve communication and collaboration, and integrate developers and infrastructure administrators.
But contrary to what many think, Dev Ops is not just automating processes but goes far beyond that. Its central pillar is part of an organizational culture that allows for achieving the goals shared by the entire IT value stream.
Only with cooperation between areas, flexible processes, and constant feedback does the DevOps culture enter a company to stay and contribute effectively to the business.
Some argue that Dev Ops is a culture, others believe it is a methodology, some say it is a position, and others see it as a new way of thinking. On the other hand, scholars consider DevOps an evolving term that should not be limited.
What is Python?
Python is a high-level programming language — or High-Level Language —, dynamic, interpreted, modular, multiplatform, and object-oriented — a specific way of organizing software where, roughly speaking, procedures are submitted to classes, which makes it possible to greater control and code stability for large projects.
It can also be extended to make system calls for almost all operating systems and to run code written in C or C++. Due to its ubiquity and ability to run on almost any system architecture, Python is a universal language found in many different applications.
In addition, all modern versions of Python are copyrighted under a GPL-compliant license certified by the Open-Source Initiative.
Why is Python so used in DevOps?
When the agile methodology is used correctly during software development, a bottleneck usually occurs during the day-to-day operation and deployment phases. New fixes and updates are produced quickly in each sprint so infrastructure teams can be overwhelmed with constant deployments (releases).
To eliminate some of these issues, operations personnel and application developers must work more closely together to automate the delivery of code to production from the release of each new version.
As DevOps is an approach to change and agility, engineers must master several. The Python programming language is one of the most critical components of the DevOps toolchain.
Ansible and other tools are written in Python, which means you can create custom scripts or modules to automate your tasks, for example, or do other things.
Python’s accessibility and flexibility are why this language is so famous for the DevOps toolkit. If you are working in DevOps, you should know how to work with multiple languages.
See other reasons that make the Python language widely used in the DevOps world:
Python is easy to read and learn. It’s also easy to copy, paste and run. Python language allows you to do some complex things without having to understand everything that is going on entirely.
Python can give you a solid foundation for those who want to explore new tools and languages and are curious about the technology. It doesn’t require the levels of commitment that a specialized language does.
It is an excellent scripting language, allowing scripts to be written locally and run anywhere, saving the need for individual scripts for all different systems.
- No guided programming
There is no need for object-oriented programming. No structured coding is required. You can go straight to take what you want and get the job done, much like shell scripts.
Python x Ruby:
Python and Ruby are often compared, and with good reason: both are highly accessible and used in applications developed by many organizations.
They also appear in the DevOps toolset. We can do almost everything with Ruby that we do with Python. But in syntax, Python has the upper hand, as it’s much more straightforward than Ruby.
When it comes to learning to debug problems and code, Python has an advantage over Ruby, as it has a much more direct approach to programming.
Companies need a consultancy to support the planning and implementation of new tools and processes involved in DevOps practices.
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