DevOps Infinity Loop-DevOps processes simplified for beginners

Partick Debois invented the term DevOps (Development+Operations) in 2009. It’s method, or more accurately, culture, in which developers and operations teams work together. DevOps is delivery pipeline that allows teams to build and deploy software continuously and make timely security and feature updates. The continuous integration and development process is referred to as the DevOps infinity loop by certain industry executives. In the DevOps infinity loopthere is no such thing as waterfall or final phase.

DevOps Infinity Loop

However, it is up to each company to create its own DevOps infinity loop: plan and document processes ahead of time. Also, if something isn’t working, don’t be afraid to iterate on those processes. Here are some crucial phrases to keep in mind when you plan your own DevOps implementation.

DevOps Infinity Loop Key Phases:

Planning- This stage is where development and IT operations teams, as well as other stakeholders, decide on the list of features to be included in each project phase, as well as an iteration value and criteria. To participate in the development plan, developers, system administrators, product management, marketing employees, and technical writers all need a seat at the table.

Project plans should be stored in a safe, centralized location like Atlassian Jira or Confluence. Furthermore, every team member should be able to access the project plans at any time and from any location.

Code. Some teams divide the code and build processes. Figure out what works best for your company. Depending on workforce availability and other project considerations, don’t be hesitant to iterate on these parts of your DevOps process. 

During the code phase, developers do the coding tasks that have been given to them. Generally, they use some IDEs to code their assigned tasks. They check their work into a centralized source code repository, such as GitLab or GitHub, as they finish their jobs, which must act as the single source of truth for code. While committing the changes sometimes other static code checks like Sonarqube check runs on the committed code. Sonarqube is used for continuous inspection of code quality. 

Build-The build step comprises using an automated program like Chef or Puppet to retrieve software code from a centralized repository. This automation tool generates software code into a binary artifact, runs functional tests, and stores the artifact in a centralized shared repository. Generally, these repositories are configured in such a way that after each commit the code is automatically built. For example, you can refer to this video:  Integrate with GitHub: Build after each commit

Continuous integration is a DevOps software development strategy in which developers merge their code changes into a common repository on a regular basis, followed by automated builds and tests. The build or integration stage of the software release process is most commonly referred to as continuous integration, and it includes both an automated component (e.g., a CI or build service) and a culture component (e.g. learning to integrate frequently). Continuous integration’s main goals are to detect and fix defects faster, increase software quality, and shorten the time it takes to validate and deploy new software upgrades.

At the end of the development cycle, DevOps eliminates the all-hands-on-deck manual method.

Testing. DevOps testing relies on intelligent automation, but it won’t replace the software development team’s human testers. Developers can achieve continuous testing by using tools like Selenium or JUnit to test various codebases in parallel. An automated testing technique assures that an application’s functionality is flawless.

Automated testing also generates comprehensive reports on the codebase. Stakeholders in a company can use this reporting to learn more about the development cycle and product maturity.

Continuous deployment. At the end of the development cycle, DevOps eliminates the all-hands-on-deck manual method. Instead, every code change is immediately routed through the complete pipeline and deployed to production with continuous deployment. Based on the requirements and pace of its team, a company can schedule as many deployments per day as it requires. In this process generally, all the deployments are automated, so after each build generated output along with all the dependencies is deployed into target servers. 

Operate. IT administrators oversee software in production during this stage of the DevOps process. Management and data collecting capabilities, as well as operational views into production applications, are provided by tools like Ansible, Puppet, PowerShell, Chef, Salt, and Otter.

Continuous monitoring. The development and operations teams should keep an eye on their production applications at all times. All organizations require access to information about the application’s health. Install collaboration and communication channels as well to alert all teams to any production concerns that arise.

New Relic, Datadog, Grafana, Wireshark, Splunk, and Nagios are some of the most popular monitoring tools for this phase.

DevOps Infinity Loop Case Study: Netflix

Netflix is an excellent DevOps case study since their software engineering process demonstrates a core understanding of DevOps concepts as well as an emphasis on quality aspects via automation-assisted methods. Recall that DevOps advocates a laser-like focus on quality qualities to satisfy business goals while employing automated procedures for consistency and efficiency.

Amazon Web Services (AWS) hosts Netflix’s streaming service, which is a big distributed system. Netflix developers had to focus hard on the quality qualities of reliability and robustness for both server-side and client-side components since there are so many components that have to operate together to provide reliable video streams to users across a wide range of devices. In summary, they came to the conclusion that the only way to become comfortable with failure is to practice failing all the time. Netflix developers set about automating failure in a typical DevOps style to attain the needed degree of confidence and quality.

If you’ve ever used Netflix software on a computer, a game console, or a mobile device, you’ve probably observed that, while the program is remarkably reliable, the accessible video streams change from time to time. The ‘Recommended Picks’ stream, for example, may not be present at all times. This occurs when the AWS service that serves the ‘Recommended Picks’ data is unavailable. Your Netflix application, on the other hand, does not crash, does not throw any errors, and does not suffer from any performance decrease. Netflix software just ignores the stream or presents an alternate stream, resulting in a perfect, elegant failure experience for the user.

Devops Infinity Loop - simian army

Netflix changed its engineering process drastically to reach this result by developing Chaos Monkey, the first of a series of tools collectively known as the Netflix Simian Army. Chaos Monkey is a software that runs in the background in all Netflix setups, wreaking havoc by shutting down server instances at random. As a result, Netflix developers are continuously working in an environment of unreliable services and unexpected outages while building code. This turmoil not only provides a unique opportunity for developers to test their software in unexpected failure scenarios but also encourages them to construct fault-tolerant systems, making their day-to-day jobs as developers less irritating. This is DevOps at its best: changing the development process and automating it to create a system where behavioral economics favor delivering high-quality software. Netflix developers will build their systems from the outset to be modular, testable, and extremely resilient against back-end service outages in order to create software in this type of environment.



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