Python is classified as a primary technology employed by teams practicing DevOps. It's a leading programming language that is no longer limited to just software development. Any top industry, technology or practice one can think of has some use of Python. But what is Python for DevOps?
Python's accessibility and flexibility has rendered it a great fit in the world of DevOps. Be it data visualization, enhancing workflows with custom utilities or web application building by the entire team, Python enables it all. Here is a complete guide for beginners looking to understand the optimum utilization of Python in DevOps.
DevOps comes from development and operations, which are the two departments it brings coordination to. It's not a tool, neither is it a technology. It's often classified as a practice, a methodology. Its goal is to enhance a company's ability to rapidly deliver services and applications.
The faster time to market is everything that can drive success in an organization. It leads to better customer experience, which itself is a big factor for a company to enjoy success. DevOps uplifts the entire organization by removing the barriers between these two departments.
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Python and DevOps together can change a lot of things for an organization. It's being extensively used in different types of developments, making it a great choice for DevOps. Here are certain key reasons to use Python for DevOps-
Many people get confused about where to optimally use Python and DevOps together. The former can have a dramatic effect on optimizing the latter's processes. This is done by simplifying and automating the development workflows. Let's take a closer look at DevOps and Python together.
Python's extensive frameworks and libraries make it apt for developing all sorts of software applications. Its modules enable interaction of databases to perform core CRUD operations. Modules like Gitapi facilitate developers in interacting with version control systems. The OS module aids in interacting closely with the basic complexities of the operating system.
This is a highly multi-tasking language. It has various use cases in different development domains, ranging from data analysis to web development and machine learning.
Developers regularly interact with cloud service providers (CSPs) like AWS, Microsoft Azure and Google Cloud Platform (GCP). The goal is to enable programmatic creation and modification of cloud resources. Python eases tasks like configuring networking, managing cloud storage and launching virtual machines (VMS).
Python's Boto3 module is excellent for cloud automation. Developers can streamline operations by integrating this language into the cloud automation workflow. This leads to reduced manual intervention and consequently better flows.
Python significantly simplifies planning and configuration management in DevOps by automating Infrastructure as Code (IaC) and configuration tasks. Its gigantic library offers support via scripting capabilities, making it brilliant for critical DevOps processes. It deploys software and configures servers well with tools like Ansible.
Its extensive libraries are particularly useful for developers during the planning and info-gathering phase. Data manipulation, analysis and cleaning are performed with Python to create data visualization.
The deployment process involves tasks like configuring software and copying files, which can be handled easily by Python scripts. This language integrates swiftly with configuration management tools (like Fabric & Ansible) for flawless server configuration.
Manual efforts are reduced as Python integrates into the CI/CD pipeline. Complex deployment is simplified, improving the entire deployment process.
Python is equipped with all necessary tools needed by a developer in relation to frameworks and libraries. It aids in building automation processes by utilizing libraries (like Selenium) and writing scripts. This promotes a seamless execution of the process.
Pytest, a popular Python library is employed to test the system. It further aids in creating automated and manual test cases that prove to be highly beneficial in finding any bugs.
Most organizations already have their own monitoring tools. However, a customizable solution for alerting and monitoring is needed every now and time. This can easily be solved with Python's SDKs. Python scripts are worthy for automating everyday operation and monitoring processes. Other libraries like psutils can be used for checking and monitoring errors in the software development process.
Various different DevOps tools are employed during different stages of the DevOps process. These include tools like Kubernetes, Git, Jenkins and Docker. Having an in-depth understanding of Python makes it easier for the professional to work and adapt to these tools. Customizing and implementing them becomes simpler.
There are many imperative cloud-native tools that are developed in Python and employed in DevOps. These include Ambassador API Gateway, Apache Libcloud and Docker Compose. Google Cloud and CLIs (command-line interfaces) are also built with Python.
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Python utilizes its dynamic tools and libraries for automation and enhanced scalability and reliability. Here's a list of the top Python tools and modules to automate various DevOps processes.
Pandas is a leading Python library best fit for data manipulation and data analysis. This library has many simple data structures like data series and frames. These are utilized for handling and analyzing structured data. Data must be effectively cleaned, transformed and analyzed for data preprocessing and exploration.
Pandas is built to handle gigantic data amounts in an efficient manner. Consequently, useful insights and information is extracted from the data.
JavaScript Object Notation (JSON) is a popular, lightweight data-interchange format. It's widely used for storing and exchanging data between a client and a server. With Python's JSON module, developers can work on JSON data to carry out tasks like writing and reading JSON files. It also enables exchanging data on the web.
Selenium is Python's open-source library used to create automation scripts. These scripts are further utilized in different browsers with the aid of drivers. This gives developers HTML elements and the prowess to perform actionable tasks like clicking a button or filling a textbox. It's particularly useful in the DevOps process since automation scripts are built on top of Selenium.
Getpass is Python's famous module known for offering secure means of reading sensitive information. This could be anything like passwords or other user information without it being displayed on the screen. It's majorly used by command line scripts and applications that need user authentication.
Scapy is an important Python library that is employed for analyzing and sending network packets. This enables developers to achieve network protocol manipulation, exploration and customization. Each of these achieved targets render it a highly valuable tool for all network administrators.
Two important aspects of script and programming are text manipulation and pattern matching. The Re module, which is a brilliant built-in module, comes handy in such situations. It's often picked for tasks like text processing and data validation in scripts and programs.
Requests simplify HTTP requests to web services. This is achieved as web interaction is simplified for developers. Requests boasts a user-friendly interface for receiving and sending data from the server, and consequently rendering it to the app. Web-related tasks are enhanced with the different cookie handling and HTTP methods provided by Requests.
Sys refers to a Python module that offers access to system functions and parameters. It's used extensively in fine handling and scripting. It's useful in system-level operations as well as environment interaction within Python programs.
Smtplib is Python's standard module employed for sending emails via SMTP (simple mail transfer protocol). This library enables the professional in creating and sending emails via the SMTP server. Additionally, email alerts, communication and notifications can also be automated.
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DevOps engineers who aim at succeeding in their endeavors must strive to always keep learning. Python is a great skill to have in one's skill set. Since the perks and tools are already discussed, it's time to move on to the roadmap. Here are the steps that can be followed by a DevOps engineer to learn Python.
It's important to begin by understanding and learning about the fundamentals of the Python programming language. This includes but is not limited to functions, control flow, data types and syntax. This can be done through various means like books, online tutorials or Python training courses.
It's imperative to gain in-depth knowledge of the OOPs fundamentals in Python. Being an essential Python concept, it's utilized in different Python frameworks and libraries. Some common fundamentals of this aspect are inheritance, classes and objects.
Python has many important web frameworks that are useful for DevOps engineers. Flask and Django are two names that cannot be missed by anyone looking to gain prowess in this programming language. Both these frameworks, and others, are utilized extensively for building web apps and even RESTful APIs.
There are many important IaC tools like CloudFormation and Terraform that must be learned to become a pro in this field. With knowledge of these, DevOps engineers can define infrastructure as code. This makes managing and deploying cloud resources easy.
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There are multiple popular Python libraries and frameworks that are utilized in DevOps. These include Ansible, Docker-py, Fabric, Boto3 and PyYAML. Every library mentioned here, and many others, aids professionals in managing configuration, deploying applications and automating tasks.
Cloud computing is a big part of both Python and DevOps. There are many cloud providers but it's only important to know about the leading ones. These are Microsoft Azure, AWS (Amazon Web Services) and GCP (Google Cloud Platform). Python is used to manage various cloud resources and consequently automate tasks.
Data science also includes many libraries in Python. The most common ones are Pandas, NumPy and Scikit-Learn. Understanding these data science libraries is crucial for machine learning, artificial intelligence and data analysis.
CI (continuous integration) and CD (continuous deployment) tools like Travis CI, Jenkins and GitLab CI/CD are highly useful. These tools aid in automating the process of software development. They also enable building, testing and deploying the apps automatically.
Python is completely jam-packed with amazing libraries and frameworks, along with extensive community support. This language has an imperative role to play in the DevOps process, especially in terms of collaboration, automation and scalability. There are endless perks of using Python for DevOps. This is why an increasing number of DevOps engineers are moving towards learning this language.
Learning Python will prove to be a rewarding voice for DevOps engineers. With the kind of opportunities and possibilities this language brings to the table, it's rapidly becoming an indispensable asset here. While there are endless tools, frameworks and libraries in Python, it's not possible to learn it all at once. The best bet here is to take one step at a time and never put a full stop to the learning journey.
At the very least, a DevOps engineer should have understanding and knowledge of Python data structures and syntax. This includes tuples, dictionaries and lists. This knowledge is imperative to enable these professionals to write simple scripts for various tasks like data processing and file manipulation.
Python is often chosen to be learned before DevOps because of its extensive library support, versatility and simplicity. It helps these professionals in scripting and automation related tasks. It's also helpful in orchestration and configuration management.
Many DevOps tools use Python. Apache Libcloud, Docker Compose and Ambassador API Gateway are all developed in Python and used in DevOps.
Python has proven itself to be among the best programming languages to achieve automation in DevOps. It aids DevOps teams in infrastructure provisioning, API-driven deployments and automating repetitive tasks.
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