5 reasons DevOps will be a big deal in the year ahead
Source – infosurhoy.com
The typical enterprise has become a nonstop software and data factory, operating 24×7. Technology has to be there at all times.
DevOps has been a hot topic and pursuit for a few years now. During this time, many enterprises have been attempting to put this way of working — in which development output is aligned with ongoing business requirements — in place in one form or another. But 2018 promises to be the year when it all really begins to come together and making itself known.
Why? We have reached the critical point in which software and data are driving every aspect of the business. Executives and decision makers recognize that technology needs to keep pace with the rapid changes going on in business requirements. Applications need to be assembled, re-assembled and dis-assembled on a moment’s notice.
Here are some observations from leading DevOps thinkers on what to expect in this space in the coming months:
Cross-platform engagement and IoT means more apps and devices: The typical enterprise has become a nonstop software and data factory, operating 24×7. Technology has to be there at all times, and DevOps will be essential to keep pace with creating, testing and delivering software on a 24×7 basis. “Add in the rise of internet of things (IoT), which allow for seamless transitions across smartphones, TVs, tablets and other devices,” Eran Kinsbruner, lead technical evangelist at Perfecto, writes in DevOps.com. “In 2018, industries such as financial services, health care, retail and automotive will fully embrace IoT. A critical step in delivering flawless UX is testing, and lots of it. Never has there been more to test, measure and develop than in this digital revolution we are seeing today.”
“DevSecOps:” Security is top of mind for everyone, and what is needed is a way to bake it into applications, from inception to retirement. Chris Carlson, vice president product management for Qualys, explores why “Sec” needs to be dropped into the term: “Security teams need to understand that DevOps is quickly changing how IT operates and need to partner with IT and application development teams much earlier in the planning and execution lifecycle,” he states. This requires “building security into the DevOps pipeline instead of bolting on after the fact.”
DevOps gets Agile: Diego Lo Giudice, analyst with Forrester, finds organizations that employ a combination of Agile — in which developers work in close coordination with end-users to deliver frequent iterations of software — and DevOps fare better than those doing both initiatives separately. “It is simply unacceptable for any IT organization to focus on an Agile-only or DevOps-only journey,” he says. “They are two sides of the same coin, and one completes the other.” Forrester’s latest survey finds enterprises with combined Agile-DevOps initiatives are up to twice as likely to report greater business/IT alignment, improved functional quality, faster business value, continuous delivery, and greater predictability of results aligned with requirements
Frequent new releases will require even faster updates: With rising expectations about enterprise technology comes pressure to ensure constantly high-performing applications. IT leaders “must recognize the importance of empowering developers with the tools and time to implement continuous testing across the software development lifecycle,” says Kinsbruner. “Tools, such as those powered by automation and the cloud, increase efficiencies and free up time developers previously spent on manual quality-checking, allowing them to ensure the apps they are producing are keeping up with consumer expectations.”
Artificial intelligence and machine learning may begin to make a dent in DevOps. Solutions coming on the market employ AI and machine learning to not only help DevOps teams track progress, but also predict where and when code is needed. In articles published over the past year, both Ronald Van Loon and Daniel Cronin discuss AI-based solutions that add cognitive computing power to DevOps processes. For example, Van Loon describes technology that “uses machine-learning algorithms to match human domain knowledge with log data, along with open source repositories, discussion forums, and social thread. Using all this information, it makes a data reservoir of relevant insights that may contain solutions to a wide range of critical issues, faced by IT operations and DevOps teams on a daily basis.”
Still, some thinking needs to be done before AI can seriously lift some burdens off DevOps teams, Kinsbruner cautions. “Developers first must understand what they want AI to help them accomplish–and how–within the SDLC and across the DevOps pipeline. One logical place to start is figuring out how they can best leverage it to analyze test automation strategies.”