By 2023, the DevOps industry is expected to expand at a pace of 24 percent, totalling USD 10.3 billion. DevOps services and solutions are in great demand due to the rising requirement for quick application delivery with high quality. As the world of software approaches its all-time high in 2020, DevOps has become the primary emphasis for influencing it.
Now that DevOps has entered its second decade, the focus has expanded beyond product delivery. It's no longer just about dev and ops, but about removing the constraints between the business and its customers, with a focus on delivering not just new features and products, but also value.
DevOps has come a long way, and there is no doubt it will continue to shine this year. Since many companies are looking for best practices around their digital transformation, it's important to see where leaders think the industry is going.
Let us read about the next big things in Devops and the future that it holds!
Enabling Cloud- Native construction
The cloud-native strategy is the driving force behind business digital transformation. This technology promotes resilience, readability, and readily knitted architectures, resulting in changes with predictable consequences, improved innovation, next-level transformation, observability, and a rich user experience. It boosts cloud automation, which helps with configuration and installation management. Cloud-native is the way to go in business for greater dependability, lower costs, faster deployment, better resource usage, and better decision making.
According to IDC estimates, cloud service spending would reach $530 billion by 2020, more than double current levels.
Services for container registry Handling artefacts is no longer a challenge. For a painless development cycle process, manage data (images), store them in repositories, and handle all dependencies surrounding them via container registries. When working with containerized applications, using a container registry in DevOps is unavoidable. This registry is also becoming increasingly essential as the popularity of cloud-native applications grows, as it provides better security.
Container and Serverless computing
With physical machine dependencies, developing and deploying apps on time has always been a challenge. Without the need for actual servers, serverless computing comes to the rescue. This is not to say that there are no servers; rather, there are cloud-based servers that assist you in allocating machine resources. It facilitates resource scalability, flexibility, and automation, which saves time and money by eliminating the need for pre-provisioning and maintenance.
CI pipeline to Assembly pipeline
The goal of a DevOps assembly line is to link activities from multiple teams that contribute to a project's optimal conclusion. It aids seamless delivery by automating and scaling procedures across teams. Because it streamlines the workflow across the pipeline, it eliminates the need for human intervention, increasing efficiency.
The DevOps assembly line, often known as "pipelines of pipelines," is a tremendous progression of DevOps in which the CI pipeline is simply one element of the line. Each pipeline belonging to a certain team communicates with one another in order to exchange data and finally achieve continuous delivery.
Data science accepts DevOps
In many ways, the data science and software development cycles are similar, and they often try to streamline their workflow with agile development practises in the past, which led to challenges in acquiring data, building models, evaluating, and maintaining consistent results in both machine and production platforms. To address these challenges, data scientists implemented DevOps, which had previously aided the software development cycle. DevOps equips them with the tools they need, as well as analytics, cross-functional communication, change management, and a dependable testing plan. This aids data scientists in improving data gathering, deploying algorithms, and increasing the value of their company.
AI Alters DevOps culture
Artificial intelligence is about analysing, integrating systems, and processing data to increase functionality, whereas DevOps culture is about automating activities across the software delivery cycle. It aids in the reduction of human error while processing large amounts of data. It increases the efficiency of software testing, collects data from multiple sources and collates it for improved dependability, improves execution efficiency, and better manages resources. AI combined with DevOps identifies issues and automatically fixes them without affecting the application's performance.
Analytics and Automation
DevOps will depend on more developed and self-sufficient techniques to generate automated productions across various stages and activities within the lifecycle. To that end, robotic process automation tools will occupy the DevOps ecosystem and help with automation of manual and error-prone tasks for physical productivity. In addition, augmenting digital apps and automating end-to-end user flows and testing will become a reality in a codeless manner. This will allow teams to cut down their test automation time. Code reviews for better validation of post-code commits will also join standard unit testing and human code reviews. These will better enable the identification of more complex security, functionality, and performance issues.
We've seen a few successful efforts in the past, and now we have a number of new trends that, given the current market climate, are most likely to succeed this year. As time goes on, we learn more about DevOps, which leads to new ideas, technologies, and tools. As a result, we've come up with the above trends that will dominate this year. As you implement these current trends, you will be one step ahead of your competitors and will rank high in the market.