The ever increasing reliance on cutting edge software has driven a surge in demand for DevOps in recent years. In turn, this has led to a greater focus on security. Gone are the days when software developers would test every aspect of their product prior to launch. These days, the expectation is that, when new technology becomes available, users have access to it straight away.
So it’s important to keep on top of the latest advancements both in DevOps and security. The two go hand in hand. Developing software at the speed at which the modern world turns, requires that security is part of the development process, not something to add later. So, what are the top 5 trends in DevOps for 2021?
1. Microservices to replace monolithic architecture
Traditional monolithic app architecture was designed to handle multiple tasks. It was reliable but it was also static, and time-consuming to develop. Now, this wasn’t a problem when companies expected to invest in systems that would remain the same for years. But monolithic architecture is no good for on-the-hoof development and flexibility so there has been a marked shift away from systems that require centralized servers and towards microservices. This new breed of software development creates applications that include suites of services which can be deployed independently.
Microservices are a modern style of software architecture which creates simple, small, pieces of software focused on tackling problems in a particular part of the organization.Microservices provide speed and flexibility. They are easy to adapt, extend, test and deploy, making them quick to market and flexible to changing needs.
These days, containerization is employed as a means of packaging an application along with the necessary libraries, frameworks, and configuration files for it to be run on various devices. Such developments caused security concern when first mooted as an option, but a general acceptance of cloud-based software as the future, and robust security measures have convinced many users that microservices provide the necessary speed and efficiency in software development without compromising on safety.
2. DevSecOps to shift left on security
DevSecOps is the natural heir to the software throne. The need for speed in DevOps has led to the creation of ‘Security as Code’ to create improved solutions that fit within an agile framework. Outdated security practices end up forming a bottleneck as they cannot keep up with demand. DevSecOps drives enhanced communication between departments integrating security into all the software development processes.
While this practice of shifting security left may appear to slow the initial process, it has been proven to speed up the delivery of secure codebases in the long run. It also allows companies to gain maximum benefit from cloud-based services and prevent unnecessary downtime.
Inherent DevSecOps safety measures increase the speed and agility of security teams and create flexible systems which can be adapted to changing circumstances or new threats. By improving collaboration and communication between different facets of the company, including distributed teams, it also helps cement a culture of togetherness across previously disparate parts of an organization.
Automation in builds, and quality and assessment testing negates the risk of human error while freeing up time for software developers to focus on high-value work.
3. AIOps implementation will rise
DevOps has joined the rest of the world in harnessing the growth of AI to enhance solutions and this trend will continue as the functionality improves. According to research by Gartner, 40% of DevOps teams will incorporate application and infrastructure monitoring tools with AI by 2023.
AIOps frees up valuable time for IT operators through:
- Pattern analysis of complex applications - As tech stacks get bigger, their complexities are magnified and the number of data points at which errors might occur increases. For an individual to work through and identify the cause of any issues would take an inordinate amount of time. Machine learning allows us to build models that analyze data and identify patterns and abnormalities. In so doing, it helps prevent problems occuring, fix issues that have arisen and suggest ways of optimizing the program.
- Tracking and behavior security - AI monitors user behavior to monitor where improvements can be made to enhance user experience. It also identifies performance issues and security threats. By spotting potential vulnerabilities and where hackers are trying to gain access, security can be tightened.
- Increased automation - AI is not designed to replace human engineering but to enhance it. Building systems that take on mundane, repetitive tasks and spot inconsistencies makes those tasks less onerous and negates the risk of human error. In some instances, AI can also provide the fix, but where it can’t (or you don’t want it to) it gives individuals more time to spend on those fixes that require human intervention. In this way, AI helps ensure organizations get the most out of each of their digital and human resources while moving forward, boosting CX and improving ROI.
4. Chaos Engineering will prevent chaos in delivery
The constant drive for better, faster and smoother end-user experience can only be achieved via chaos engineering. In fact, despite its name, the one thing you don’t get with chaos engineering is chaos. Remember the days of discord between various departments within a single organization? Supposedly near-complete products sent to the next department who found faults, suggested improvements and sent software developers back to the drawing board? While on the face of it such practices may have appeared ordered, there is nothing more chaotic than different arms of the same body moving in different directions.
Chaos engineering utilizes the latest advancements in security and protection to test software in real-time and identify problems before they rear their heads. In turn, this encourages collaborative working, agile methodology and quick improvements. By 2030, it’s predicted that 40% of DevOps teams will be working this way – with an anticipated reduction of 20% in unexpected downtime. That sort of improvement can be expected to increase productivity by up to a fifth.
5. DataOps to establish itself as a dominant force
Since the dawn of business, data has been the key driver in establishing trends, identifying areas for improvement and making informed decisions. But today, the amount of data available in all aspects of business is far greater than ever before – giving business leaders and DevOps teams alike, access to new ways of obtaining, interpreting and actioning information.
DataOps uses machine learning to foresee outages, analyze performance and make accurate predictions as part of the development process of new software. This predictive analysis is the future of DevOps and the future of work.
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