3 Areas to Drive DevOps Change
In 2019 and beyond, DevOps will be increasingly customizable via cloud & containers, machine learning/artificial intelligence, and security
When it comes to the software development life cycle (SDLC), the ability to edit a process, customize an inherited process, and track the number of customizations is considered sacred. With DevOps, the SDLC is especially manageable, partly due to the emphasis on teamwork, collaboration, and synchronization between business units.
The ever-growing popularity of DevOps means an increased fine-tuning of the overall DevOps process. This customization of DevOps is already gaining traction, and we can expect to see more in the years ahead. The DevOps community is already finding some trending behavior, such as capitalizing on the ability to leave feedback, or soliciting feedback directly on github. Process models for projects are discussed at length on some user boards, with members of the projects exchanging best practices and user stories.
When integrated properly, DevOps has the potential help businesses attain hugly competitive advantages in the marketplace. With that said, take a look at 3 innovative ways units are customizing their DevOps to get the most out of their daily grind.
#1: Cloud and Containers
Making life easier for the end-user begins with the developers in software delivery. By focusing on cloud and container based solutions from ideation to execution – or in the case of software delivery, from the testing phase to production, developers can make room for increased self-service, which is very popular among end-users.
What’s more, developers used to spend hours creating painstaking, elaborate scripts detailing various tasks, tedious to-do lists – creating models to describe each and every one of their work processes. Now, with the introduction of self-service capabilities, these laborious, pre-emptive efforts may be curtailed or done away with entirely, simplifying the process.
An example of this simplicity exists in the execution of commands. When trying to build a Kubernetes pipeline for instance, commands such as “get entire data set” ensure faster, smoother operations for teams involved in the SDLC; whereas previously, these commands were faulty or did not exist at all.
#2: Machine Learning and Artificial Intelligence
We can expect to see an increase in machine learning and artificial intelligence integration in tech trends for 2019. According to a DevOps strategist and expert, emerging DevOps operating systems merge concepts involving artificial intelligence and machine learning.
The focus predominately relies on next-level predictive analytics, with efforts resulting in the production of authentic, valuable business deliverables. Moreover, a focus on the best application of both Machine learning and artificial intelligence will become hot items in the near future – with 2020 being the year where they will likely shine.
Ultimately, machine learning and artificial intelligence will promote efficiency throughout every phase within the DevOps lifecycle. One may ask, could tasks be done faster? What are methods to reduce the cycle time in a pipeline, from inception to production? Now, assuming the pipeline lasts two days, if competitors offer same-day processing, you can see the difference in competitive advantage. Harnessing Machine Learning and AI will help curb the competition, by completing this process in just one single day or less, other than the typical two days.
#3: DevOps Security
Prioritizing security when it comes to DevOps is a trend we will definitely see gaining traction in 2019. The buzzword “DevSecOps” (Dev Security Ops) may become a household term in the coming years.
The time is nigh to stop the practice of adopting vulnerable frameworks susceptible to attack. Security integration will become part of the lifecycle upon inception, rather than an afterthought – as DevSecOps sees it’s way into the pipeline upon the beginning of development.
Prior to the production phase, security teams should reinforce efforts in checking and validating code to maximize safety and prevent problematic encounters.