DevOps and the Origins of DevOps

In 2007 Patrick Debois accepted an appointment with an Belgian ministry, where his job included transferring to a data center. Debois had a keen desire to know each aspect of IT infrastructure and his work in QA (Quality Quality Assurance) obliged him to switch from his development and operations areas frequently. Sometimes, Debois would be working in the development team's planning process and participating on Agile development using tools for developers such as adobe, etc. Sometimes, he found himself in the operations team fighting problems, making sure that production was operating efficiently and making sure that code was properly deployed. The constant switching between the two groups illustrated the stark differences between development and operations cultures , and Debois realized that there was an easier method for them to work together. To know more about DevOps culture consider taking Post graduate program in DevOps from reputed program.

Operations and development were typically distinct "siloed" roles within an organization prior to the introduction of DevOps.

Debois was introduced to the similarly-minded Andrew Clay Shafer at an Agile conference in 2008 and their lively conversations established the basis for the later development of DevOps.

After we've gone over the past, we're able to concentrate on the nature of what DevOps is and the things it doesn't. First and foremost, DevOps is a human problem-specifically, that of a historical lack of communication and collaboration between developers, IT professionals, and QA engineers (and, more recently, information security professionals). Thus, the adoption of DevOps results in a significant cultural shift in which IT, developers, IT, and QA interact and collaborate on regular regularly, breaking down the barriers that existed previously between these two groups. If this change in culture is not made, DevOps cannot succeed.

Let's be clear: a cultural change this large is a challenge, and won't happen overnight. Learning the steps needed is simple, but the implementation of them is a different scenario. Additionally, for a successful adoption of DevOps 100% participation from the management team is required. If management continues to insist on employees to continue to be ensconced in the old methods (where "old" refers to the year 2008! ) A tentative DevOps implementation will be a disaster.

As we're still not fully identified DevOps We should begin with the idea that DevOps encompasses a set principles that promote increased communications and cooperation (among other changes in culture). These principles are typically explained in the CALMS model of Culture Automation, Lean measurement, and sharing. Like before the understanding of these concepts is simple however changing one's behaviour to accept these principles is not easy.

Ensuring Continuous Integration as well as Continuously Deployment.

An organization that manages an effective DevOps environment efficiently is awarded continual integration (CI). It is an aspect of the development lifecycle where there is a continuous flow of development deployments from the base code of production.

Instead of the long and complicated traditional 48-hour deployments continuous integration enables developers to quickly solve issues, make adjustments as needed, and continuously test.

CI is the heartbeat of lean, agile, and other management concepts. CI results in more efficient software, happier users and healthier businesses however, for CI to be effective it is essential to centralize a lot of the typical "dev" as well as "ops" tasks so that everyone on the team works together. This is the challenge of management of DevOps.

The tension between Dev and Ops

Operations teams have traditionally been concerned with issues such as the user environment, server states load balancing, managing memory. They must keep things in a stable condition within a continuously changing environment. Developers on the other hand are entirely about constant deployment and changes. The process of getting these two teams to cooperate is an enormous task. As we'll discover numerous technological advancements have been made that will help us conquer this issue.

Version Control Technologies

The first days of DevOps saw the development of technology for controlling version like Git, Github, Bitbucket, and SVN. These tools were actually in use prior to the advent of DevOps but they've acquired the importance they have gained under DevOps. Instead of having to ensure they have the right version of the code the operations team has been deploying code that's checked in and developed every day. Every piece of code that is deployed has to be tested for integration before it is put on a production machine. Version control provides an online connection between teams of developers and operations so that it's an easy process for them to "roll back" unapproved code to restore production machines to their original state.

Automating deployments and Continuous Integration

Continuous integration tools like Jenkins, TeamCity as well as Travis permit the development of code and tested right after it's checked in effectively automating testing and deployment processes. Since automation is the main principle of DevOps the tools allow for more efficient integrations that don't rely on humans, and together with systems for controlling versioning and version control systems, they permit easy rolling back in the event of mistakes are discovered.

Cloud Services as well as Configuration Management

A different issue is apparent when you consider the prevalence of cloud technology in the modern development lifecycles. The first time ever production servers can be built and destroyed at will with platforms like Amazon Web Services, Azure or Google Cloud Platform which allows for elasticity in load balance. Before cloud servers became the norm, companies bought physical servers to be able to handle the largest computing demands that they had in mind. This is the equivalent of having enough warehouse space to support "Christmas level" processing, but using the majority of the space, but only only a couple of times per year.

When cloud-based servers come online We must make sure they have a similar configuration with the current servers in production server(s). The technology employed in managing the servers is tools for managing configurations like the Puppet, Chef and Ansible. These tools were designed to control the configurations of huge numbers of servers using an easy-to-use scripting-like programming language. They create machines descriptions ("infrastructure in the form of code") which are stored and then retrieved from the version control system and quickly applied to hundreds, tens, and even thousands of computers. If the desired configuration changes generally, it's an easy task to release the new configuration to all the machines within our infrastructure.

Containers and Microservices

Continuous deployment and integration are based upon the concept of modularization. This is the notion that a thousand little changes are better than a single big one, and that developers must try to separate their code in order to make these small modifications. The notion of redeploying the entire code base over a long period of time has been abandoned.

Practically speaking this means that instead of creating monolithic apps of the past today, developers are developing applications that are based upon microservices-small modular, flexible, and easy-to-replace applications. Microservices-based architectures allow the easier deployment of continuous applications.

Container technologies like the Docker as well as LXC are founded on a fundamental idea, namely developers can contain (or "containerize") the application (or microservices that are component of an application) together with the dependencies required to run the application.

Instead of relying upon "golden images" virtual machine, programmers are able to simply put their work into containers that can later be placed into production environments as totally independent microservice applications.

Containers are able to isolate the software they hold from host machines. Therefore, all dependencies for the program reside within the container, and can't conflict with versions of these dependencies that might exist on host machines. The concerns regarding the state of the servers at any time cease to be relevant as the containers run regardless of the location they are placed. Another benefit is that the containers themselves can be disposed of. They're environments that start the application, run it, and then go away. Instead of creating an application within any environment, programmers have the ability to create an environment around an application.

Controlling Containers and Microservices

When we deploy massive numbers of containerized microservices quickly it is essential to be able to manage these microservices. An abstraction level is required for an operation team to efficiently create and maintain microservices that are within a ecosystem for production. cluster Manager tools (also known as Orchestration tools) like Docker Swarm, Kubernetes as well as Mesos are specifically designed to assist in this. These tools permit the rapid and efficient deployment of microservices to several nodes in a cluster permitting administrators to manage the rapid implementation and distribution of huge numbers of containers in a multi-node system.

Where is DevOps moving?

As you will see, the current DevOps environment isn't just one thing. It is a system made up of various technologies and merged to provide an effortless transition of code. This is mostly accomplished by the design. The main feature of any design framework is its capability to disconnect the widget from the machine and then plug into a new one at the click of a button. An instance would be to replace Puppet with Ansible , or replacing Bitbucket with Gitlab in a flash. The ability to make such rapid changes gives engineers the freedom to utilize ever-changing technologies and then plug into the system with minimal interruption to the operation of the process. However there's a cost for doing this.

The first is the human factor. In order to become a competent DevOps engineer, you need to be proficient in many different technologies. An effective DevOps engineer might be setting up AWS machines this morning while writing bash scripts in the morning and rolling back any changes to version control the next day (or the three tasks on the same day). The ability to accomplish all of this, and doing it effectively is a difficult task for engineers of any level. We're in a position where both the developers and the operations personnel must be proficient in code, and also have a thorough understanding of of the various aspects of an deployment. What can we do to solve this problem?

Management technology has been developed in recent times to assist in integrating these diverse technologies into one seamless technology system that can be controlled by these technologies. This is what is known as "abstraction."

Abstraction lets us go through these more intricate processes and remove them which means we'll require fewer people to manage them effectively.

Sometimes, developers may need to enter production machines to make bash-based code or modify an existing Docker container that's not working correctly, but generally abstraction can provide us with an instrument that will perform the bulk of the work involved in scheduling prioritizing, coordinating, and ensuring the smooth flow of all of the tasks within this system. It's much simpler to comprehend and use the tool than to comprehend the specifics of every single process it controls.

We are seeing the rising popularity of integrated environments like Cloud Foundry, Spring as well as Sonatype. These environments permit DevOps teams to run an efficient, integrated DevOps chain while also offering an identical "plug and play" adaptability that is available in the current configuration. Many companies are incorporating integrated environments to streamline the DevOps workflow.