Top 5 DevOps predictions for 2017
2016 was good for DevOps: We saw more enterprise adoption of containers and more organizations throwing their hats into the container ring. That doesn’t mean that the tools surrounding DevOps are mature. According to experts, 2016 set the stage for security enhancements, containerization, and consolidation.
The next phase of DevOps will focus more on security. In 2017, building security practices as code will be part of application development, rather than applying it post-facto. This will lead to DevOps going beyond Dev, QA, and Ops. So, explore this article and the top five DevOps predictions for 2017.
TOP 5 DEVOPS
Considering technology on front, we will see increase in popularity of containerization solutions Docker because of its ability to provide constant environment from development to production. Next year it will be more popular with non-production environments, and as it develops it will see similar popularity for production environments. One of the key reasons for this popularity is its portability across multi-cloud platforms.
EFFORTS TO ADDRESS ENTERPRISE CONCERNS WILL INCREASE
With an increased experience of implementing next gen platforms and automatically generating containers, there will be a greater focus on enterprise concerns, such as access controls, audit trails, and network technologies that can implement “virtual firewalls” at the level of the orchestration tier.
DevOps will extend into pay-as-you-go
We will see more cloud implementations of DevOps to meet the needs of an on-demand model. Technology solutions which orchestrate across cloud providers will only accelerate that adoption by abolishing the risk of cloud provider locking. Customers can easily switch over to a low-cost provider and can profit from the elastic nature of the cloud pricing model.
MORE AUTOMATED CODE
In 2016, organizations began introducing tools to decrease the tedium of finding a line of error code in applications, and 2017 will see more automation for developers. The automation will revolve around code testing, gathering and formatting data, reporting, and notifications.
“Coding through automation and machine learning will be more dominant than previous years, but now it is possible due to new hardware and techniques” such as GPUs and parallel computing.