DevOps Automation processes: recommendations and advantages

DevOps Automation Processes: Recommendations and Advantages

There are many advantages to DevOps automation processes. Automation can help to speed up the process of delivering new features and updates to your customers and can also help to reduce the chances of human error. Automating DevOps processes can also help to improve communication and collaboration between development and operations teams.

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Some recommendations for DevOps automation processes include using a tool like Jenkins to automate the build and deployment of new code changes, using configuration management tools like Puppet or Chef to automate the provisioning and configuration of new servers, and using monitoring tools like Nagios to automatically detect and diagnose problems.

Implementing DevOps automation can help your organization to improve its speed, quality, and efficiency of delivery, and can ultimately help to improve your bottom line.

What is DevOps?

ToDevOps Automation is a set of principles and procedures for software development that aim to reduce cycle time and improve efficiency by facilitating processes like continuous integration (CI), continuous delivery (CD), and continuous deployment (CD). In order to succeed in today’s business climate, DevOps automation teams and site reliability engineers need to be able to provide frequent updates and new features with quick turnaround times without compromising the reliability of the production environment.

One of the central tenets of the DevOps Automation mindset is the notion that software features should be delivered by multi-disciplinary product teams that have the resources to build and maintain the features themselves. You Build It, You Run It is the DevOps Automation mantra encapsulating this idea.

Once a new feature was developed, it would be handed off to the IT operations team, who would be responsible for testing it out in production and providing ongoing support and maintenance. DevOps automation calls for a multidisciplinary product team to oversee the development and deployment of products, involving both software engineers and IT operations personnel. This results in less downtime, faster releases, and fewer defects by breaking down the barriers between the software development and IT Ops teams and sharing their knowledge more effectively.

DevOps automation is defined as follows:

DevOps automation is the use of specialized software tools and methods to automate repetitive, labor-intensive tasks that are part of the software development lifecycle but don’t need to be done by hand.

For what reasons is DevOps automation crucial?

Automation is a cornerstone practice for DevOps teams because it supports and strengthens every other practice. By letting team members automate routine tasks that they do often, it improves cooperation and communication by giving them more time to work together instead of doing hard work by hand.

Making sure everything runs well and consistently is a huge help for observability, improvement, and shift-left practices. It complements declarative configuration management as well.

• To what end does DevOps automation serve?
• There are many good things about automation, and they all help make DevOps a reality
• Consistency

With advanced DevOps automation, outcomes are reliably predictable and repeatable. As long as it is not changed, a software automation tool will continue to perform the same actions. This is not true, though, for processes that need human help and are therefore more likely to go wrong.

Speed

Integration of code and deployment of applications are both accelerated by automation. There are two main reasons why this is so.

The first benefit is that the process may begin immediately, rather than having to wait for a person to finish something first. There’s a chance that relying on a human to deploy a new release at 2 in the morning won’t work. No waiting is necessary while using automation software.

Additionally, the execution time of automated operations is typically shorter. An engineer must evaluate the environment, fill out configurations, manually check that the latest version was successfully deployed, and so on in order to manually deploy a new release. Instead, these tasks can be accomplished in a matter of seconds with the help of an automated program.

Scalability

To scale, you need automation, and automation gives birth to scalability. Even if a task can be done by hand in the short term, it is usually not practical to do it by hand on a large scale.

If you’re only managing a single app in a single production setting, for instance, you might be able to deploy new versions manually. When your team is managing multiple applications and deploying to multiple environments, like more than one cloud or different operating systems, it’s hard to get new code out quickly and reliably.

How do I know which DevOps tasks to automate?

DevOps automation is a broad concept that encompasses a wide range of processes and techniques, some of which are shared throughout companies and others that are unique. While it would be ideal to automate everything, this is rarely possible due to competing demands.

Each DevOps automation group will have to make this choice based on its own unique circumstances. DevOps professionals use software tools (like Docker and Kubernetes) to improve efficiency at every stage of the software development process. Some typical activities that can benefit from DevOps automation are enumerated below.

Plan

DevOps automation teams spend time in the planning phase figuring out the product’s or feature’s business and application needs. This involves deciding on metrics to use for measuring performance, deciding on a security strategy, and designing a release plan. In addition, we use input from key stakeholders, customers, and product road maps to direct our work going forward.

In this step, people often use Jira, GitHub, GitLab, Asana, Atlassian, iRise, and Azure DevOps.

Code

Here, a single developer takes over the work of several earlier planners and produces tangible results in the form of code and/or configuration artifacts. Developers commonly use source code repositories for tasks such as code review and revision. To prevent developers from overwriting one another’s work, the source code repository keeps track of the different revisions of the code that have been checked in.

At this stage, Git, GitLab, Subversion, Cloudforce, Bitbucket, and TFS are the most popular tools.

Build

This part of the pipeline involves compiling the source code repository into executable artifacts and then running automated unit and regression tests to ensure the code is stable and ready for deployment. Prior to releasing a product or feature, teams utilize metrics to assess its readiness for release, including the quality and performance of its code, the speed with which its builds are completed, and other factors. A crucial best practice for continuous integration is the automation of the build process. Teams need the option to kick off a comprehensive build process that does everything from compiling binaries to creating docs, websites, and metrics.

At this stage, popular tools include GitLab, GitHub, and CFEngine.

Test

The goal of software verification is to ensure only high-quality features are released to users. Unit tests, acceptability testing, regression testing, the study of security and vulnerabilities, testing of configurations, and monitoring of performance are all a part of this process. At this point in the software development life cycle, test automation software and static security analysis programs are used.

To guarantee the source code is still behaving as expected, automated self-testing should be executed on every build. If a bug is found, it can be fixed by debugging, or the code can be reverted to a stable version from before with only a small loss of functionality.

Publish and roll-out

A new function is ready for release once it has been tested and found to be functioning as intended. The final process is “staging,” or packaging the release for distribution. Some of these procedures, such as package configuration, may require management and executive input during the approval process. Package management tools are used by DevOps automation teams in order to stage and hold releases. Examples of such tools are JFrog’s Artifactory and ProGet.

Even after a program has been released to production, it may still need tweaks to its settings to ensure peak performance. Provisioning the network, configuring the release, and setting up any necessary applications or data storage are all examples of these tasks. Ansible, Chef, and Otter are just a few examples of the infrastructure-as-code and configuration automation tools used by DevOps teams.

Keep track of and manage

The impact on the end user can only be understood through performance and security monitoring after a release has been configured and optimized. To make sure the feature works as planned, teams must keep an eye on the health of the IT infrastructure that supports it, use established metrics to measure the quality of the user experience, and record data from the production environment.

A fast-paced DevOps automation environment makes it challenging to keep track of all the moving parts manually at scale. Automation solutions that can track issues with availability, performance, or security and send out notifications are one solution to this problem. Sumo Logic is a paid tool for DevOps teams that uses machine learning to make application monitoring in production more efficient and effective.

To what extent can DevOps automation best practices be automated?

Obstruction, generally speaking, is not possible to fully automate a DevOps automation workflow. DevOps automation does not eliminate the need for engineers. The most well-automated DevOps systems still need human monitoring and involvement when things go wrong or when upgrades are required.

But teams should try to automate as much as they can because it cuts down on the time people spend on routine, low-level DevOps automation tasks.

Merge the DevOps toolset

The rising difficulty of delivering software is directly correlated to the proliferation of disparate pipelines and toolchains, both of which obstruct visibility. Due to the high degree of fragmentation among different technologies and teams, there is no simple way to centrally measure and optimize the efficiency of the software delivery pipeline in real time. Each tool provides background on its performance, but they don’t easily share data, making it tough to gain a holistic understanding of the business.

Sumo Logic allows teams to monitor their real-time software delivery and development performance, compare it to industry standards, and continuously enhance it. We expand observability to include software delivery mechanisms.

We make it possible for development teams to automatically correlate data across their CI/CD pipelines in order to continually measure and optimize their software delivery performance. Leading cloud development tools like AWS, Azure, GCP, Jira, GitHub, Bitbucket, Jenkins, PagerDuty, Opsgenie, and many more are integrated with our solution.

You can get it up and running in no time at all, allowing your team to work together more efficiently and push out better, more reliable code more quickly. The DevOps automation Research and Assessment (DORA) group made a Key Performance Indicators (KPI) method that we use to automatically get industry-standard metrics backed by actionable insights and raw logs. The goal is to give teams full visibility and observability throughout the entire DevOps lifecycle.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.