DevOps saw a lot of container adoption by enterprises and more companies bolstering their offerings in 2016. Security improvements, containerization, and consolidation are on the horizon for next year.
This past year was good for DevOps: we saw more enterprise adoption of containers and more companies 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 improvements, containerization, and consolidation. Here are five expert predictions as we head into 2017.
According to Joan Wrabetz, CTO at Quali, the DevOps tools market is ripe for consolidation, mainly because of consumer demand and the sheer volume of companies out there. Currently, the proliferation of DevOps tools means that it's difficult to integrate all of them. "To simplify the process, consumers will choose a few best of breed tools and then try to wrap as much of their processes as possible around these tools. This will lead to more tools that provide end-to-end functionality across the continuous delivery cycle," she said.
Additionally, because of the growth of current DevOps companies, 2017 will bring acquisitions of smaller companies. "We have already seen Amazon make an investment in Chef. Also, a number of DevOps companies... have raised significant capital, and they will be under pressure to get a return on that investment," Wrabetz said. She believes this consolidation is much-needed in the tool market.
We saw a lot about containers in 2016, and Greg Willis, Director of Technology Operations and Systems Architecture at Morpheus Data, believes that, as tools become more stable and the container ecosystem starts to consolidate under orchestration tools, we'll see even more containerization. "Just like with virtualization technology that helped enterprises get comfortable with virtual machines moving from bare metal, we will see more adoption by enterprises to run workloads on container platforms like AWS ECS, Kubernetes, DCOS, and Docker Datacenter," he said.
Unified development, security, and operations
If 2016 was the rise of security, particularly with Docker's updates, 2017 will see the unity of development, security, and operations, according to Aruna Ravichandran, Vice President, DevOps Solutions Marketing, CA Technologies. "Given the increasing intensity and sophistication of attackers - and how rapidly digital compromises turn into bad publicity and potentially irreparable brand damage - code cannot be good without being safe and deployed within a solid security architecture," she said.
As microservices and SDKs evolve, Ravichandran believes that building in security features from the start will be easier for developers, without sacrificing the user experience. However, when it comes to testing and deploying code, she said that security validation should be viewed as a special case. "[T]he requirements of security-related code testing are highly idiosyncratic and dynamic and will likely involve experts and constituencies ... not normally part of the DevOps process," she added.
More security, period
DevOps may be the latest vulnerability for enterprises, according to Reuven Harrison, CTO and Co-founder of Tufin. In 2017, he expects to see more security applied to the DevOps process to ensure compliance. It will be a challenge to make security a priority in the fast-moving DevOps world, but if it doesn't happen, 2017 may be the year of a major data breach caused by the DevOps approach.
"We may see a major breach that gets tracked back to the DevOps approach, causing DevOps and security teams to become new best friends," Harrison said.
More automated code
Last year, companies began introducing tools to decrease the tedium of finding a line of buggy code in applications, and 2017 will see more automation for developers. This won't be a bad thing, according to Job van der Voort, VP of Product at GitLab, since the automation will revolve around code testing, gathering and formatting data, reporting, and notifications.
"Coding via automation through machine learning will be more prevalent than years past, now made possible due to new hardware availability and techniques" like GPUs and parallel computing, he said.
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