2019 DevOps tech forecasts are two months away, but some predictions are set stone when it comes to DevOps. New estimates from IDC suggest that the DevOps software market will grow from its 2017 results of $2.9 billion to $6.6 billion in 2022. What will drive that growth?
CI/CD is being shaken up by container and container orchestration technologies
In 2019, configuration management systems such as Chef, Puppet, and Ansible will be largely replaced by container orchestration tools such as Docker, Kubernetes, and Helm Charts. Docker offers many advantages for applications, while Kubernetes is essential to the successful management of large container deployments. If done right, container orchestration tools can simplify many of the complexities of working with infrastructure. DevOps engineers will have to adapt to this new toolchain. — Jawahar Malhotra, senior vice president of engineering, HackerRank
Continuous Delivery approach will expand due to experimentation
Adoption of the continuous delivery engineering approach and use of container related-technologies (such as Docker and Kubernetes) in large organizations will dramatically increase with the adoption of microservices and multi-cloud architectures. In addition, fully automated continuous deployment (leaving the entire chain of continuous integration (CI), continuous delivery (CD), and continuous deployment on autopilot) will get traction by companies embracing immutable infrastructure approaches and technologies (such as Spinnaker) to manage services and software deployments. With the increased adoption of process mining techniques and technologies, in 2019 we will also see more teams experimenting with how process mining discovery, compliance, and performance enhancement can help DevOps teams to learn and improve their CI/CD workflows. — Miguel Valdes Faura, CEO and co-founder, and Charles Souillard, CTO, COO, and co-founder, Bonitasoft
Data Science teams adopt DevOps as AI use increases
Companies that have implemented DevOps have seen increased business efficiency and faster deployments. Consequently, we foresee that, in 2019, as the demand for AI-driven applications continues to rise, that data science teams will adopt DevOps best practices in their model management workflows. A DevOps approach helps data scientists build automated pipelines to re-train, re-select, and re-deploy production models in a more stable way while providing the ability to test multiple models deployed into production. This trend will accelerate as data science and application development teams begin working closely together to improve the efficiency of developing, deploying, and maintaining AI & ML-driven applications to meet demand across the enterprise. — Justin Charness, principal product manager, machine learning, Oracle
Deeper focus on Functions
In today’s DevOps environments, technology professionals who have mastered operating containerized workloads in complex ways are working to streamline and optimize delivering these capabilities by leveraging functions as a service. The breadth and depth of this focus on functions will likely deepen throughout the next year, as more technology professionals become comfortable leveraging containers in production, and recognize benefits achieved through serverless computing—such as faster start-up times, better resource utilization, and finer-grained management. However, even with these recognizable benefits, future DevOps pros will become adept at determining the use cases where functions as a service and serverless computing are appropriate for their environments and resources. Without this acquired skill, companies that dive right into functions-as-a-service without understanding the benefits and pitfalls of running numerous individual functions for different tasks may see bigger bills as the result, and the tech pro trying to explain these bills to management and business leaders may see bigger problems. — Keith Kuchler, VP of engineering for SolarWinds Cloud
DevOps is at a crossroads, the future looks quite different
I think containers are going to become the atomic unit of application execution—the expected standard by both Dev and Ops—and those that are not container-native, for instance, a monolithic app running in a virtual machine, will require specialized teams and experience. Adding to this will be serverless functions and these will coexist and be mingled with the containerized microservices creating an increasingly powerful and flexible application architecture, but one which is also more difficult to build and manage. These shifts will necessitate a change in DevOps tools and workflows. As cloud- and container-native applications become the norm, I think the toolchain will naturally follow. Developers will no longer want or need to either install tools or write code on their local machines. Web-based, polyglot IDEs delivered through a SaaS experience (even if that is facilitated by a private enterprise cloud) will be the new default. — Brad Micklea, senior director and lead, Developer Business Unit, Red Hat.
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