This article is based on reporting originally published by RCR Wireless News (rcrwireless.com) and references findings from the 2017 Puppet State of DevOps Report.
DevOps: Still Evolving, Still Expanding
DevOps—the practice of integrating software development and IT operations teams into a unified, collaborative workflow—has spread rapidly across organizations of all sizes. The core promise is straightforward: break down the silos between Dev and Ops so that software can be delivered faster, at lower cost, and with higher quality. Despite significant momentum, DevOps remains a discipline in flux. Its success depends not just on tooling, but on cultural alignment, process redesign, and organizational buy-in. The five trends below reflect where DevOps has been heading and why each one still deserves attention.
1. Microservices Architecture
The adoption of microservices accelerated significantly as teams looked for ways to decompose large, monolithic applications into independently deployable units. Rather than deploying a single large codebase, microservices break functionality into discrete services, each running in its own isolated environment with its own allocated compute and memory resources.
This isolation makes microservices a natural fit for DevOps workflows:
- Independent deployment: Teams can release individual services without coordinating a full application deployment.
- Targeted scaling: A single high-demand service can be scaled horizontally without scaling the entire application stack.
- Clearer ownership: Small, cross-functional teams can own specific services end-to-end, reinforcing DevOps accountability models.
- Composability: Discrete microservices can be combined to build larger, more complex systems incrementally.
Containerization platforms such as Docker and orchestration tools like Kubernetes have become tightly coupled with this trend, providing the runtime infrastructure microservices need to operate reliably at scale.
2. Tool Consolidation
The DevOps toolchain ecosystem is vast. Configuration management tools such as Chef, Puppet, Ansible, and SaltStack each offer overlapping but distinct capabilities. Add in CI/CD platforms, monitoring stacks, artifact repositories, and secrets managers, and the result is a sprawling, difficult-to-maintain toolchain.
Organizations have responded by consolidating toward a smaller set of well-integrated, “best-in-breed” tools rather than adopting every available option. This consolidation trend also drove significant M&A activity—Cisco’s acquisition of AppDynamics for approximately $3.7 billion is one frequently cited example of a major vendor expanding its DevOps and observability portfolio through acquisition.
The practical benefits of consolidation include:
- Reduced cognitive overhead for engineers managing fewer tools
- Lower licensing and maintenance costs
- Improved integration between pipeline stages
- Easier onboarding for new team members
3. Security Shifting Left (DevSecOps)
Security has historically been treated as a gate at the end of the software delivery lifecycle—reviewed by a separate team shortly before release. DevOps practices have pushed security earlier into the development process, a shift commonly called “shifting left” or, more formally, DevSecOps.
The 2017 Puppet State of DevOps Report highlighted a meaningful performance gap tied to this approach. According to that report, high-performing teams who had integrated security practices into their delivery pipeline spent approximately 50% less time remediating security issues compared to low-performing teams—a finding consistent across both the 2016 and 2017 survey years.
Practical DevSecOps patterns include:
- Static application security testing (SAST) integrated into CI pipelines
- Dependency vulnerability scanning on every build
- Infrastructure-as-code security linting (e.g., checking Terraform or Ansible playbooks for misconfigurations)
- Threat modeling during the design phase, not after deployment
4. Deeper and Broader Automation
Automation has always been a foundational DevOps principle, but the scope of what gets automated has expanded considerably. The 2017 Puppet State of DevOps Report found that high-performing organizations automated significantly more of their software delivery pipeline than low performers—spanning configuration management, testing, deployment, and even change-approval workflows.
Key areas where automation delivers outsized value in a DevOps context:
- Configuration management: Ensuring infrastructure state is defined in code and applied consistently (Ansible, Puppet, Chef).
- Testing: Running unit, integration, and end-to-end test suites automatically on every commit.
- Deployment: Eliminating manual handoffs in the release pipeline through continuous delivery pipelines.
- Approval workflows: Using policy-as-code to automate low-risk change approvals, reducing bottlenecks without sacrificing governance.
The cumulative effect is a reduction in toil—repetitive, manual work that does not scale—freeing engineers to focus on higher-value problem solving.
5. Big Data and Machine Learning in the Delivery Pipeline
Automated software delivery pipelines generate enormous volumes of data: build logs, test results, deployment events, performance metrics, error rates, and user behavior signals. Managing and extracting value from this data has become a discipline of its own.
Organizations have started applying machine learning and data analytics to this telemetry to:
- Identify patterns in build or test failures before they reach production
- Predict deployment risk based on historical change data
- Automatically surface anomalies in application performance metrics
- Generate actionable insights for decision-makers without requiring manual log analysis
- Integrate operational data with business analytics platforms for end-to-end visibility
This intersection of DevOps and data engineering is increasingly referred to as AIOps—applying AI and ML to IT operations data to enable faster, more informed responses to system events.
Looking Ahead
These five trends—microservices, tool consolidation, DevSecOps, expanded automation, and data-driven operations—reflect a consistent underlying direction: reducing friction, increasing feedback, and making software delivery a reliable, repeatable engineering discipline rather than a heroic, manual effort. Organizations that invest in these areas methodically, with an eye toward culture as much as tooling, tend to see the most durable improvements in delivery performance and operational stability.