Top 7 DevOps Trends to streamline production

By 2025, more than 85% of organizations will have adopted a cloud-first approach to improve customer reach and service-delivery efficiency. As organizations increasingly migrate to the cloud, they must adopt DevOps trends to keep up with the ever-changing requirements and complexities of modern software production.

This article highlights several DevOps trends and discusses their relevance to software development, operations, and security teams.

What is DevOps?

DevOps is a software development approach that prioritizes collaboration between the development and operations teams. As global organizations are constantly faced with the need to produce the most competitive products at the fastest pace, bringing these teams together helps them find a balance between agility, cost-effectiveness, scalability, and speed.

By doing away with traditionally siloed teams, DevOps integrates three stages of the software development life cycle (SDLC)—development, testing, and deployment. The result is a single, efficient process where developers and operations teams have access to the product simultaneously. This facilitates healthy coding: Each team works with the other in mind, thus eliminating handovers, associated delays, and errors while encouraging shared responsibility.

Fig. 1: DevOps effectively combines development and operations teams. (Source: ManageEngine)

Aside from uniting the development and operations teams, DevOps also implements techniques and technologies (jointly referred to as trends) that streamline activities and processes throughout the SDLC.

Several DevOps trends exist that help facilitate rapid, agile, and reliable software deployment. In the following sections, we cover these along with the technologies that facilitate them and their benefits.

DevOps trends examples: CI/CD and Kubernetes

Two key examples of DevOps trends include continuous integration and continuous deployment (CI/CD) and Kubernetes. DevOps teams leverage CI/CD to automate repetitive processes in the SDLC, thereby enabling faster testing, bug identification, remediation, and product delivery. They also utilize Kubernetes to deploy and manage containerized workloads, enabling consistent deployment across heterogeneous environments.

For example, a DevOps team member can write, test, and make changes to an application’s code using a CI/CD pipeline, then run, manage, and scale containers with Kubernetes. The team can also leverage the automation features of both to hasten patch propagation and restart failed containers, reducing the need for manual intervention and improving resource efficiency. Put together, these two trends deliver shorter development cycles, fewer glitches, and faster mean time to repair (MTTR).

Microservices architecture

Microservices enable software development where apps constitute multiple independent but loosely connected services. The services, each representing different application functions, execute requests independently; however, they communicate through lightweight APIs that allow them to work together where necessary. Since each service functions independently, the need to develop and deploy all services simultaneously is eradicated, accelerating time to market (TTM).

For example, a banking application can have a separate service for funds transfers, payments, balance inquiries, currency exchange, and loan servicing. The funds transfer service is responsible for all intra- and inter-bank transfers; the payments service allows customers to calculate and pay taxes, subscription fees, or utility bills; and the balance inquiry service tells customers their account balance, details their transaction history, and provides them with their account statements.

Where necessary, these services can interact; for instance, the payments service interacts with the balance inquiry database to confirm that customers have the required funds in their accounts before they can complete a transaction. Additionally, the bank can choose to develop and release all services in the app at once, or it can release critical ones first to hasten TTM, then continuously release others while the app is in use without impacting user performance.

Microservices architecture brings several core benefits, as well as some limitations.

Fault isolation

With traditional monolithic software deployments, the failure of one component (or maintenance on it) can result in substantial downtime for the entire app. However, the independence of microservices allows DevOps teams to isolate faulty services. Similarly, in the case of excessive traffic in one service, only the affected service is slowed down or rendered unavailable.

Let’s consider the banking app again. If the payment service is down, customers can still transfer funds, exchange currencies, or request their account statements. This ensures software reliability and prevents business losses.

Scalability and streamlined software development

In a monolithic architecture, where one component has reached its resource limits, scaling it means the entire app must be scaled. With microservices, DevOps teams can scale only the required service or provision new services on the go.

DevOps teams can also use the best technologies to provision each service. For example, you can use a conversion API for real-time updates in your banking apps’ currency exchange service. You can also use different database types for specific services as required; for instance, you can use a combination of traditional relational databases (e.g., MySQL, PostgreSQL, or Oracle) and document-oriented/NoSQL databases (e.g., Elasticsearch or MongoDB) to record transaction history and store other account information.

Limitations and mitigation strategies

Despite its important benefits, microservice architecture is not without its downsides. For one, root cause analysis can be cumbersome since there are multiple interconnected services. Identifying the source of an error, the exact service it emanated from, and how other services were impacted can be time-consuming. Also, because the services are independent, observability can be tricky; you must correlate telemetry from all services and enrich them with contexts to fully understand the performance of your distributed system.

However, there is a solution to these problems. OpenTelemetry provides a standardized API and SDK to collect telemetry data, correlate them, and export them to vendor-agnostic telemetry backends to troubleshoot your app.

Overall, microservices positively tip the scale, facilitating development-operations collaboration and improving efficiency, scalability, and product delivery speed—without irreversibly impacting observability.

DevSecOps

DevSecOps integrates security practices and assessments into software development throughout the SDLC. It is a shift-left approach based on the belief that since security is a frontline concern of cloud deployments, issues such as secure coding, code assessment, compliance assessment, and change monitoring/management must be prioritized.

DevSecOps enables a proactive approach where developers can identify and resolve security vulnerabilities early on via the use of static application security testing (SAST), interactive application security testing (IAST), and software composition analysis (SCA) tools.

• A SAST tool scans your app’s codebase for security vulnerabilities before it is deployed and offers guidance on implementing secure coding practices.
• An IAST tool runs automated tests on running apps to detect and resolve security issues in real time.
• An SCA tool scans software components (e.g., app source code, container images, and other open-source packages) for vulnerabilities to proactively detect and alert DevSecOps teams.

Adopting the DevSecOps approach from the onset of your SDLC will limit the risk of security vulnerabilities getting past the development stage and causing significant damage (e.g., privacy breaches, financial losses, and reputational damage) post-deployment.

DevSecOps emphasizes the need for a strong security posture via data encryption and vulnerability scans before software components are added to running apps. This ensures that the detection and remediation of security issues is efficient and cost-effective; it also facilitates compliance with cybersecurity standards such as the ISO 27000 and NIST 800 series.

Shifting-left testing

Shift-left testing is a software testing approach that moves testing to the left of the SDLC timeline. Traditionally, software testing was done after the software had been fully developed and was ready for deployment. But this approach was often problematic, with errors and bugs getting past testing undiscovered or getting discovered when remediation would require time- and resource-intensive measures (e.g., rebuilding the app).

Since it becomes difficult to achieve complete visibility into an app and its several first- and third-party components after the development phase has been completed, shift-left testing helps DevOps teams eliminate as many bugs and vulnerabilities as possible early on. To ease the repetitive tasks involved, DevOps teams should embrace automated, AI-powered testing tools. By automating shift-left testing, DevOps teams can localize and remediate bugs and regression defects quickly, seamlessly, and cost-effectively.

Shift-left testing improves the quality of your code and software, which in turn speeds up app deployment and provides your users with an optimal web interface.

GitOps

GitOps is an approach to implementing DevOps practices, including automated code reviews, testing, and patching. GitOps entails using tools such as Git and SVN to enable version control and provide central code repositories considered single sources of truth (SSOT) for app code and infrastructure as code (IaC) development and management. So how does this work?

Say, a DevOps team discovers a bug during testing and needs to make changes to their app’s codebase. The team creates a local branch of the Git or SVN repository serving as their app’s SSOT. New code changes are made in this local branch and are subsequently merged to the central repository by opening pull requests. After this, they create a CI/CD pipeline that allows them to integrate and validate the changes, then automatically test them for regression defects before deployment.

GitOps automates the building, testing, and patching of software, aiding faster and more efficient production, deployment, and debugging. By allowing for incremental code changes and version control, GitOps enables DevOps teams to track code changes over time. This can be exceedingly important for debugging apps, especially when performance or security glitches are caused by previous patching operations.

With its branching approach, GitOps also enhances DevOps collaboration, enabling multiple teams to work on a single infrastructure without version ordering or scheduling glitches related to patches.

Containerization

Containerization is the practice of putting applications, app components, and dependencies within isolated containers for faster and more efficient deployment in heterogeneous environments. A container is a logical, executable software package that comprises an application and all of its components (e.g., configuration files, software code, libraries, and dependencies) that are necessary for its continued functioning. The container helps to isolate an application from its runtime environment.

Containers are created via immutable container images and are orchestrated using tools like Kubernetes and Docker. Orchestration tools automate the deployment of, communication between, and management of containers, allowing for faster production and deployment. Moreover, orchestration platforms provide features such as self-healing for pods (which run containers) and comprehensive health checks to ensure the continued, optimal performance of your runtime environment. They also offer improved software reliability through dynamic pod creation and deletion; new pods can thus be created and old pods can fail without negatively impacting other existing pods.

With containerization, multiple containers can run on the same host operating system, each as an isolated unit. This isolation feature is achieved by running an app and its dependencies on a detached, virtualized host OS. It is also this feature that allows you to build your apps once and move them to/run them consistently in any environment, ensuring interoperability and agility.

Serverless computing

Serverless computing is a function-as-a-service (FaaS) model that abstracts the need for developers to manage their own servers; instead, they rely on third-party services that dynamically provision and manage their servers on demand. Contrary to the name “serverless,” physical servers are still used but on a pay-per-use basis. This way, DevOps teams can focus on writing, testing, and deploying code while service providers manage the underlying infrastructure.

The serverless trend is based on the containerization/microservices model, where apps are divided into functions that can be provisioned and scaled individually. This reduces TTM since developers can roll out services as required, rather than all at once. With its backend-as-a-service feature (BaaS), serverless architecture provides support for polyglot apps.

Also, with serverless, organizations do not have to deploy their own servers where they have more bandwidth than required, nor do they have to deploy and manage multiple servers due to workload size. This brings cost savings to organizations, as they pay per bandwidth or number of servers used.

Conclusion

In this article, we have presented several DevOps trends that can dynamically revolutionize your software development, deployment, and maintenance process. While each has unique use cases and offers varied functionalities, they are interwoven.

If jointly applied to your SDLC, these trends will offer numerous benefits, including simplified software development, faster deployment for a competitive edge, improved user experience, and, ultimately, an enhanced bottom line.