DevOps enterprise usage ratio is increasing year by year

Ever since the word DevOps was introduced, it has focused on the communication and cooperation between Development and Operational personnel, which has greatly improved the integration and implementation efficiency of programs and services. According to statistics, the adoption of DevOps by business organizations has increased by 35% from 2015 to 2018. This also means that the company has evolved from a barn effect that was originally too specialized in division to a mode of breaking the gap and changing to work with each other. From the perspective of the growth over the years, the proportion of corporate organizations accepting DevOps culture will continue to increase in 2019. Nextlink has compiled relevant information and will tell you the possible future development of DevOps in this article.


The next possible stage of DevOps

Microservices is being used increasingly by companies

Due to the rapid development of Container technology in recent years, microservice technology is allowed to be implemented, which made related technologies such as DevOps and CI/CD very favorable. Microservices are distributed architectures. Therefore, when a problem occurs in a certain part of the program, there is no one to launch the whole system. As long as the problematic part is removed, the service interruption can be avoided. Microservices architecture helps developers easily deploy and scale. Before adopting a microservices architecture, enterprise technology decision-makers should understand why a microservices architecture is used.

Kubernetes will flourish

Kubernetes (K8s) is the fastest-growing container technology due to the many features and benefits of its products. For developers, the fast, easy-to-deploy features of the container have been used in recent years. As the best carrier of the “microservice architecture”, the characteristics of the container and the DevOps concept complement each other. The service that supports Kubernetes on AWS is EKS.

Security is an important factor that cannot be ignored– DevSecOps

 At the beginning of the software development cycle, “security” is considered an important consideration, not an additional feature. Effective DevOps ensures fast and frequent development cycles (sometimes weeks or days), but outdated security protection may even make the most effective DevOps program fail. In the collaborative architecture of DevOps, security is a shared responsibility of every member of the DevOps team. This is an important mindset that some people call “DevSecOps” to emphasize the need to build a secure foundation in the DevOps program. Here’s an architectural diagram of implementing DevSecOps on AWS using AWS CodePipeline:


AI/Machine Learning takes DevOps to the next level

The focus of DevOps is to automate and monitor every step of the software delivery process, ensuring that work is done quickly and frequently. Although manual work is not eliminated yet, DevOps encourage companies to build repeatable processes to increase efficiency and reduce change. AI/ Machine Learning can help with the development of DevOps, because these two technologies can handle complex tasks and a large amount of information, allowing the company’s IT staff to focus on professional tasks.

Communication and response are always one of the biggest challenges when organizations adopt DevOps. There is a lot of information in the system, and the team needs to build more kinds of pipelines to set up and modify the workflow. Using AI-initiated automation technology, chatbots, and other systems, these communication pipelines can be made more streamlined and proactive.

DevOps’ core thinking advocates “attempt quickly, fail quickly,” so it’s important to build an alert system that quickly discovers vulnerabilities. In the past, alerts were generated quickly and closely, and all conditions were marked with the same severity, making it difficult for teams to respond immediately. The machine learning application helps the team prioritize responses based on past behavior, judging the severity of the current alert, and the source of a particular alert.

AI / Machine Learning also has the potential to help developers during the application creation process. Through the success of past applications in build/compile, test completion, and operational performance, machine learning algorithms can proactively advise developers based on the code they are writing or the application they are building to improve development efficiency.

Next Generation AWS Managed Services and Automation

In a fast-changing modern world, companies that respond to customers instantly and anticipate the demand to gain a competitive advantage. For most companies, the quarterly release of new features simply cannot be released to the market in the current quarter.

Automation is a core principle that AWS believes should be the next generation of MSP escrow services. By working with AWS MSP partners, you can leverage your deep expertise in automation.

Next-generation AWS MSPs can also help you adopt DevOps principles and best practices internally, and gain the ability to focus resources and time on developing new features, rather than spending valuable internal resources on infrastructure management.