SourcDevOps consists of a collection of technologies, work procedures, and techniques intended to increase the effectiveness and efficiency of the software development lifecycle. One of the core principles of DevOps is continuous integration and delivery.
The main contributor to success of CI/CD is the creation of a team of individuals who can develop, test, and maintain software themselves. This process was created to help businesses make themselves more competitive by producing software more quickly, without sacrificing quality. Not only does this greatly speed up software development and productivity, but it also ensures better maintenance of the application. Due to the foreknowledge the team has of all aspects of the software, new features/updates can be rolled out quickly.
Why should you use Python For DevOps?
- Python is an extremely popular high level scripting language that is used widely in the fields of web development, data analysis, data science, mobile app development, and game development.
- It has extensive libraries which can be utilized for a wide variety of functions.
- Python is popular for writing automation scripts and can be used with highly popular open source tools such as Selenium and Appium to write sophisticated automation scripts.
- Python has a great supportive community, and there are a lot of forums, guides, and tutorials to aid programmers.
- Python has gained popularity for also being very useful for data visualization. Libraries such as seaborn and matplotlib can be used to create aesthetic visual figures and graphs.
- Python is excellent for implementing machine learning, and has a wide variety of specialized ML libraries such as TensorFlow and SciPy.
- All Linux systems come pre-bundled with Python, making it a defacto go-to scripting language on these systems.
- Python can be used across different development testing and production environments, making it very productive for DevOps Processes.
Popular Python Libraries and Tools to Automate DevOps Processes
Some of the popular Python Libraries and Tools that are used for DevOps Automation in Python are:
- Pandas
- Selenium
- Pytest
- Beautiful Soup
- Jenkins
- SciPy
- Behave
- Ansible
- BrowserStack Automate
- TensorFlow
1. Pandas
The Pandas module is a highly useful module for data analysis and very popular among data analysts and data science engineers. The Pandas dataframe is capable of efficiently handling large amounts of data and also allows users to gain insights and extract useful information from the data.
2. Selenium
Selenium is a very well-liked open-source Python library renowned for aiding developers design automated test cases which can be executed across different browsers through drivers.Testing the functionality of a button, or carrying out tasks such as filling out a form, navigating a web application, these are all tasks which can be done using sub-modules and drivers with Selenium.
Selenium offers a vast collection of open source tools that are helpful for all types of automation problems. Automation testing frequently takes a long time, creating test cases, carrying them out, and verifying them can be difficult. However the benefits far outweigh the drawbacks. There are so many different browser versions, operating systems, and devices that tests need to be run on, and without automation testing it would be nearly impossible to test on all browser/OS/device combinations.
3. Pytest
PyTest is a testing framework that enables users to write scalable and straightforward test cases. PyTest is well-liked because it has an easy to learn syntax, allows developers to parallely execute multiple tests, and because it is open-source.
4. Beautiful Soup
Beautiful Soup in Python is the most useful module for parsing XML and HTML data in order to obtain useful information. Filtering the HTML data by tags and gleaning statistics regarding the website are all easily accomplished by using the modules in the BeautifulSoup web scraping library. This library has several versatile uses that allow developers to navigate,manipulate, and extract data from applications efficiently.
5. Jenkins
Jenkins is the oldest open source CI/CD automation server, and thus also has one of the largest user bases. Its main claim to fame is the over 1800 user-contributed plugins it has, and how versatile these plugins are. In addition to these plugins there is also a lot of documentation available, along with guides and helpful communities to provide support for new Jenkins users.
Jenkins may be used with Python and is compatible with most popular OS such as Windows, Macintosh, and Linux. Lastly, Jenkins is self-hosted leading its users having greater control over customizing and tailoring their CI/CD pipelines according to their needs.
6. SciPy
The SciPy library is an open-source library used to solve mathematical and scientific problems in Python. This library is built on the Numpy library and therefore builds further on the features offered by Numpy, for example it is able to handle more complex linear algebra and has more features to solve such problems in comparison to Numpy. SciPy contains modules for linear algebra, statistics, image manipulation and processing, numerical integration, optimization, and other problem solving modules required to tackle scientific problems.
7. Behave
Behave is a BDD framework, a behavior driven development framework, which is integrated with automation frameworks such as Selenium. It essentially functions as a layer which defines certain behaviors to be followed in different scenarios when executing automation test cases.
Conclusion
Programming is essential in the DevOps lifecycle and a versatile, efficient, and easy to learn language like Python is representative of everything DevOps stands for.
All DevOps engineers would greatly benefit from learning Python since it can be used at every phase of the DevOps lifecycle. In addition to this several tools such as Ansible are coded purely in Python, and whenever any optimizations or add-ons need to be made to the functionality of the tool it’s best if it can be done in Python.
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