Below are four habits that effective DevOps engineers consistently demonstrate.

1. They Strive to Understand Both Sides of the Fence

DevOps sits at the intersection of software development and IT operations. Effective practitioners do not settle for expertise in only one domain — they actively work to understand the other side.

“If you have trouble writing code, focus on it and learn how to code,” Dempers says. “If you have trouble learning about hardware, focus on that. Learn about infrastructure and learn about networking, load-balancing, proxies.”

This cross-discipline fluency has a practical payoff: it enables engineers to build their own tooling — command-line interfaces, automation scripts, monitoring integrations — that bridges the gap between development workflows and operational processes. Rather than depending on others to translate requirements across the dev/ops divide, a well-rounded engineer can navigate it independently.

2. They Invest in Understanding Underlying Foundations

The DevOps ecosystem moves fast. New tools, platforms, and services appear constantly, and there is rarely enough time to thoroughly learn every one of them. Dempers argues that the most durable investment is not learning how to use a technology, but understanding why it works the way it does.

He offers a concrete example: “Instead of just learning how to run a Docker container, you dive into the Linux kernel features that make containerization possible — namespaces, cgroups, and so on. Once you understand those, how Docker works becomes obvious. You can then move on to technologies like Kubernetes, which rely on the same kernel primitives, and understand them far more quickly.”

This foundational knowledge also improves communication across the organization. When you understand how the pieces fit together at a fundamental level, you can explain trade-offs clearly, identify gaps in a proposed architecture, and make better decisions — rather than simply following vendor documentation.

3. They Actively Work to Manage Bias

Every experienced engineer develops opinions about technologies — preferred languages, favorite orchestration platforms, go-to monitoring stacks. That is natural and, in moderation, useful. The problem arises when preference hardens into tunnel vision.

“Bias toward certain technologies is fine, but if you develop tunnel vision around a particular stack, it won’t help you or your team,” Dempers explains. “Knowing and recognizing the strengths and weaknesses of the technologies you work with is a genuine superpower. It helps you make better decisions and strengthens your DevOps career.”

In practice, this means being willing to recommend a tool you personally dislike when it is genuinely the right fit for the problem at hand, and being honest about the limitations of tools you favor. It also means staying curious about technologies outside your comfort zone rather than dismissing them.

4. They Prioritize Simplicity Over Over-Engineering

One of the most persistent failure modes in modern engineering is complexity for its own sake. Cloud providers offer hundreds of services, infrastructure-as-code tools enable elaborate configurations, and there is constant pressure — from vendors, from peers, from the desire to look sophisticated — to reach for complex solutions.

“We live in a world where developers and operations teams are constantly over-engineering,” Dempers says. “Cloud providers make it easy to overspend and over-build.” The antidote, he suggests, is deliberate restraint: stepping back before designing a solution and asking a deceptively simple question — Is this actually the problem I need to solve?

Talking to the right stakeholders early often reveals that the assumed problem is not the real one, or that a far simpler solution already exists. The discipline of challenging problem definitions before investing in solutions is a habit that saves engineering time, reduces operational overhead, and keeps systems maintainable.

Putting It Together

None of these habits require a specific certification or years of experience in a particular platform. They are mindset shifts that compound over time:

• Learn both sides — close the knowledge gap between development and operations so you can work fluidly across the boundary.
• Understand foundations — invest in first-principles knowledge so that learning new tools becomes faster and comparisons become clearer.
• Manage bias — know your preferences but do not let them drive decisions; evaluate each situation on its own merits.
• Keep it simple — resist over-engineering; validate the problem before committing to a complex solution.

The common thread is intellectual honesty — about what you know, what you do not know, and what the situation actually requires. That disposition, more than any particular tool or framework, is what defines an effective DevOps engineer.

Original advice attributed to Marcel Dempers, software engineering specialist, from a public presentation on DevOps career development.

DevOps in 2019: Stability, Open Source, and Platform Growth

Change is constant in software development, yet some tools and platforms demonstrate remarkable staying power. Looking back at 2019, two facts stand out: Java and .NET — both dominant a decade earlier — remained the backbone of enterprise development, and GitHub continued to grow as the central hub of the DevOps ecosystem, even after being acquired by Microsoft.

Each of these three pillars went through significant evolution in 2019 — evolution that shaped how teams build, share, and ship software. Here is a breakdown of the developments that defined DevOps that year.

Java Redefined Itself with Jakarta EE 8

2019 was arguably the most consequential year for enterprise Java in two decades. The release of Jakarta EE 8 in September 2019 marked the first time enterprise Java was governed entirely under an open source model, free from single-vendor control.

The transition had been in motion since September 2017, when Oracle announced it would hand Java EE over to the Eclipse Foundation. The Foundation renamed the project Jakarta EE and spent two years establishing a new governance process, intellectual property regime, and release pipeline before shipping the first official release.

Mike Milinkovich, executive director of the Eclipse Foundation, described the significance to ITPro Today:

“With Jakarta EE, we are able to deliver a set of specifications that are free for implementation and are available under an entirely new licensing regime that’s far more open and cost-effective than what was available under Oracle and the JCP. The thing to keep in mind is that Java, as the language and as a platform, is still one of the largest and most widely used programming languages in enterprise computing on the planet.”

— Mike Milinkovich, Executive Director, Eclipse Foundation

For DevOps teams running Java-based workloads, the shift to open governance matters in practical terms: specifications are now free to implement, vendor lock-in risk is reduced, and the community — rather than a single corporation — drives the roadmap.

.NET Core 3.0: Open Source Gets Desktop and Self-Contained Apps

Microsoft’s .NET has been Java’s historical counterpart in enterprise development. In September 2019, .NET Core 3.0 shipped with several changes relevant to DevOps engineers and application developers alike.

Key Features in .NET Core 3.0

• C# 8 support: .NET Core 3.0 added support for C# version 8, bringing nullable reference types, async streams, and other language improvements.
• Windows desktop frameworks: Windows Presentation Foundation (WPF) and Windows Forms gained support, allowing legacy Windows desktop applications to be built and run on .NET Core.
• Self-contained executables: Applications can now ship as a single executable that does not require a separate .NET runtime installation on the target machine — a meaningful step for simplified deployment and containerisation.

Microsoft reported at the time that more than one million developers were actively using .NET Core. Much like Jakarta EE, .NET Core is fully open source, reinforcing an industry-wide trend of major runtimes and frameworks moving away from proprietary licensing.

GitHub’s Dominance Continued Under Microsoft

2019 was GitHub’s first full calendar year operating as a Microsoft business unit, led by CEO Nat Friedman. Many in the developer community had expressed concern at the time of the 2018 acquisition that Microsoft’s ownership might drive users toward alternatives. Those concerns did not materialise in measurable numbers.

At the GitHub Universe conference in November 2019, Friedman reported that the platform had surpassed 40 million developers, with more than 10 million joining in the previous year alone.

“More developers are choosing GitHub every day than ever before. Just in the last year, over 10 million new developers joined GitHub and started contributing to projects on GitHub.”

— Nat Friedman, CEO, GitHub (GitHub Universe, November 2019)

GitHub Actions and GitHub Packages

The most significant platform additions announced at GitHub Universe 2019 were GitHub Actions and GitHub Packages, both of which became generally available in November 2019.

• GitHub Actions: A workflow automation system built directly into GitHub. Teams can define CI/CD pipelines, automate testing, releases, and other repository events using YAML-based workflow files stored alongside their code.
• GitHub Packages: A package registry integrated with GitHub that supports Docker images, npm, Maven, NuGet, and RubyGems packages. It works alongside GitHub Actions and existing CI systems to provide a unified build-and-publish workflow.

Together, these features positioned GitHub as more than a code hosting service — they made it a first-class component of modern DevOps pipelines, combining source control, CI/CD, and package distribution under one platform.

What These Trends Meant for DevOps Teams

Looking at 2019 in aggregate, several clear themes emerge for DevOps practitioners:

• Open source governance is now the expected norm for major runtimes. Both Jakarta EE and .NET Core moved control away from single vendors, giving enterprises more confidence in long-term platform stability.
• Platform consolidation around GitHub accelerated. The addition of Actions and Packages reduced the need for separate CI and package registry tools, simplifying toolchain decisions for smaller teams.
• Java and .NET are not legacy concerns. Both platforms received substantial investment in 2019 and continue to underpin a large share of enterprise workloads. DevOps tooling and pipelines built around these languages remained as relevant as ever.

For teams evaluating their DevOps strategy, the lessons of 2019 reinforce that choosing well-supported, community-governed platforms and consolidating toolchains around mature ecosystems reduces operational risk over time.

This article is an independent technical explanation of Kubernetes for DevOps engineers and technical decision-makers. It draws on the subject matter originally summarised in a ZDNet overview published in October 2019.

What Is Kubernetes?

Kubernetes is an open-source workload orchestration and distribution system for clustered servers. It ensures resource availability, accessibility, and balanced execution for multiple services running concurrently across a pool of machines.

In practical terms, Kubernetes lets any number of servers — physical or virtual, on-premises or in the cloud, separated by any distance — share workloads for a common set of tenants. It then exposes those workloads to clients as services: a client system contacts a service over the network, passes data to it, and receives a response. The underlying server or servers handling that request are invisible to the caller.

This model — distributed data processing, previously locked inside monolithic applications — is what Kubernetes brings into the open, making it observable and manageable at scale.

One of Kubernetes’ most celebrated capabilities is horizontal scaling. As client demand for a particular workload rises, the orchestrator provisions more replicas of it. As demand falls, it removes them. When applications are decomposed into microservices — independent functions that communicate over the network rather than through shared memory — Kubernetes can scale individual microservices up and down independently, just as it would a complete application.

The Business Case for Kubernetes

Kubernetes is not purely a technology argument; it is an economic one. The costs of maintaining aging, monolithic infrastructure are rising, while the business case for replacing it with modern, distributed architectures is strengthening across several dimensions:

• First-generation virtualisation is ageing. Classic VM-based cloud infrastructure — where a server image runs on a remote hypervisor as if it were local — is being supplanted by container-based models that do not require software to behave as it always has. The business case for monolithic application development is eroding, even in high-complexity domains like massively multiplayer gaming.
• Service meshes are replacing address-based routing. The internet’s DNS system maps addresses to owners, not to functions. Service meshes overlay that system with function-aware maps, enabling distributed applications to locate each other across dispersed networks. These meshes are tightly integrated with Kubernetes.
• Client-side compute assumptions are shifting. Mobile applications pushed intelligence to the client partly to conserve wireless bandwidth. As bandwidth becomes less of a constraint, it may become practical to shift more logic back to the server side, enabling a new generation of lighter-weight client devices.
• Edge and distributed data centres are emerging. Hyperscale public cloud facilities serve tens of thousands of tenants from centralised locations, often hundreds of miles from end users. Highly distributable compute makes smaller, geographically dispersed data centres more viable and, in latency-sensitive applications, more desirable.
• AI workloads can be decomposed. Machine learning and inference workloads are resource-intensive in monolithic form. Distributed, containerised service models can reduce per-component footprint, potentially making AI capabilities far more pervasive in everyday software.
• Containerisation simplifies software delivery. Containers package workloads as portable, self-contained units hosted by an operating system (rather than a hypervisor). Code is pulled from a registry and deployed directly into production. This enables continuous deployment — updates daily rather than quarterly — and improves both operational integrity and security posture.

What “Orchestration” Actually Means

Orchestration is the coordinated management and execution of multiple workloads sharing an IT platform. In Kubernetes’ case, workloads often arrive pre-decomposed into microservices — independent units that cooperate but run separately. Kubernetes orchestration allows those units to be replicated, redistributed, and retired as demand requires.

A common misconception is that an orchestrator plays the role of a conductor — keeping everything in time and rhythm. That is actually the operating system’s role; Kubernetes does not change it. The orchestrator’s job is closer to that of a logistics coordinator: ensuring that every part of a composition has the resources it needs, that no single part starves the others, and that the whole performs efficiently even when its components are spread across multiple locations.

Orchestrator vs. Operating System

An operating system marshals the safe execution of programs on a single machine. Kubernetes fulfils an analogous role across a cluster: it oversees the execution of workloads across a pool of servers whose processing, memory, storage, and networking resources have been collectively abstracted.

This does not make Kubernetes a distributed operating system. Each node’s OS still governs program execution at the machine level. In a containerised environment, the container’s host is the OS — not a hypervisor, as in vSphere or KVM. Kubernetes operates one level above this, coordinating across nodes rather than within them.

Registries, Deployment, and the Concept of a Workload

In the modern data centre, software is not installed in the traditional sense. Instead, it is stored in a registry — a repository of container images — and pulled on demand. Making a containerised application available via a registry for introduction into a Kubernetes-managed cluster is called deployment.

A workload is a running deployment: one or more software components operating together as a cohesive unit with a defined purpose. Developers still write programs; but in the act of packaging those programs into containers and declaring their operating parameters to Kubernetes, the software becomes a workload whose resource consumption can be measured, managed, and constrained.

Services, APIs, and Serverless Functions

A service, architecturally, is software that accepts specified inputs and produces predictable outputs — independently of any user interface. A microservice is a self-contained, self-reliant service, typically focused on a single function. Kubernetes’ core competency is orchestrating services and microservices.

An API (Application Programming Interface) is a collection of services with a defined communication protocol, typically addressed over HTTP. Kubernetes orchestrates the services that respond to those API calls.

A serverless function is an extreme form of service abstraction: the caller does not address any specific server or cluster. The infrastructure details are resolved transparently by the platform, giving the caller the impression that the function runs locally. From an orchestration standpoint, serverless functions are workloads like any other — Kubernetes (or a serverless layer built on top of it) handles the placement and scaling.

The Components of Kubernetes

Understanding Kubernetes requires separating its control plane (the brain) from its data plane (the workers).

The Control Plane

The control plane is an isolated network of components responsible for governing the cluster. It contains three primary components:

• kube-apiserver — The API server validates and processes all incoming requests, including requests directed to services running inside pods. It is the single entry point to the cluster’s control logic.
• kube-controller-manager — A collection of controllers, each responsible for managing a specific type of cluster resource. For example, a Job controller handles batch workloads assigned to pods. Critically, Kubernetes allows additional controllers to be added, making it capable of orchestrating resources beyond containers.
• kube-scheduler — The scheduler assigns pods to worker nodes. When a new pod needs to be placed, the scheduler evaluates the current state of available nodes and delegates the pod to whichever node is best positioned to handle it.

Controllers watch the cluster’s state continuously. When an event signals a change — a node fails, demand spikes, a deployment is updated — the controller evaluates the situation and triggers the appropriate response. Operators are specialised controllers that encode the operational knowledge needed to manage complex, stateful applications automatically.

Pods: The Scheduling Unit

The basic scheduling unit in Kubernetes is the pod — a group of one or more containers that share a network namespace and storage volumes, and are always co-located and co-scheduled on the same node. Kubernetes schedules pods, not individual containers.

The Data Plane: Nodes and kubelet

Every server in the cluster — physical or virtual — is a node. Worker nodes run workloads. Each worker node runs a local agent called kubelet, which receives instructions from the control plane and manages the containers running in pods on that node. It is kubelet that interfaces with the container runtime.

Docker’s runC was for a long time the de facto container runtime in Kubernetes. The Kubernetes project has since developed its own alternative, CRI-O, which implements the Container Runtime Interface (CRI) directly and is the preferred runtime for distributions such as Red Hat OpenShift.

Manifests: Declaring Intent

Each workload is described to Kubernetes by a manifest — a YAML file that declares the desired state of the object: what container image to run, how many replicas to maintain, what storage and network resources to request, and under what conditions to restart. Kubernetes continuously reconciles the actual state of the cluster with the declared desired state in these manifests.

Custom Resource Definitions (CRDs): Extending Kubernetes Beyond Containers

One of the most consequential architectural decisions in Kubernetes is the Custom Resource Definition (CRD) mechanism. CRDs allow operators to teach Kubernetes to recognise and orchestrate resources that are not containers. Practically, this means Kubernetes can be extended to manage:

• Virtual machines — VMware has invested in making Kubernetes a first-class VM orchestrator via projects such as KubeVirt and its own Tanzu portfolio.
• Large-scale data workloads — Frameworks such as Apache Spark can be provisioned and managed through Kubernetes operators, replacing dedicated cluster managers for many use cases.
• HPC workloads — High-performance computing schedulers such as Slurm have historically governed supercomputer workloads. Kubernetes is increasingly evaluated as an alternative or complement as it approaches broader adoption.
• Machine learning pipelines — Projects like Kubeflow allow Kubernetes to orchestrate the databases, schedulers, and training jobs that ML workloads require, even where Kubernetes itself lacks native support for deterministic scheduling or large-scale parallel data access.

How Kubernetes Won the Orchestration Wars

The container orchestration market had credible competitors. The battle was decided faster than most observers expected, and for reasons that were not purely technical.

Docker Swarm

Docker Inc. ignited the container revolution and built its own orchestrator, Docker Swarm. Swarm had genuine technical merits: operators reported a significantly lower learning curve than Kubernetes, and its overlay networking model — which separated container-to-container traffic from host traffic on distinct planes — was considered elegant. In multi-cloud scenarios, Swarm’s network segmentation offered practical latency advantages.

Docker’s business philosophy — open-source core with commercial attachments it called “Batteries Included but Replaceable” — led it to found the Open Container Initiative (OCI) in 2015 under the Linux Foundation, standardising container image and runtime formats via runC. In the same week, many of the same organisations announced the Cloud Native Computing Foundation (CNCF), also under the Linux Foundation, whose first major stewardship project would be Kubernetes.

Google threw its full marketing weight behind Kubernetes, branding its managed service as Google Kubernetes Engine (GKE). In enterprise sales conversations at the time, Kubernetes was widely perceived as a Google product. Against Google’s credibility in cloud infrastructure, Docker — a much smaller company — could not compete for perception, regardless of Swarm’s technical qualities.

The CNCF simultaneously built an entire ecosystem around Kubernetes: service discovery, performance monitoring, storage management, security tooling, and more. Docker’s attempts to build a comparable extensibility ecosystem got entangled in internal architectural debates.

Red Hat’s 2015 decision to rebuild OpenShift on Kubernetes proved pivotal. By 2017 Red Hat had become a top-tier Kubernetes contributor. Microsoft hired key open-source engineers — including Brendan Burns, one of the original Kubernetes creators at Google — and rebuilt significant parts of Azure around Kubernetes. The industry alignment became self-reinforcing.

By late 2019, Docker was publicly recommending that users try running Kubernetes and Swarm side by side, acknowledging that Kubernetes had “evolved to add a lot of functionality.”

Apache Mesos

Apache Mesos was the established leader in distributed workload scheduling before Kubernetes arrived. It pioneered the master/worker cluster architecture and was extended into a private PaaS platform called Marathon. Its first major production deployment was at Twitter. In 2013, Mesos engineer Ben Hindman co-founded Mesosphere, the primary commercial vendor for Mesos, which produced DC/OS — an early contender for becoming the default orchestration layer on Microsoft Azure.

Mesos had years of production credibility. But the Kubernetes ecosystem’s momentum proved insurmountable. In 2017, VMware and Pivotal launched a Kubernetes-based platform (Pivotal Container Service). Azure followed by deprioritising DC/OS. Amazon opened its managed Kubernetes service (EKS) in 2018. IBM’s acquisition of Red Hat — and with it, OpenShift — brought another major platform fully into the Kubernetes orbit.

Mesosphere rebranded as D2iQ in 2019 and pivoted to a Kubernetes-centred strategy. The orchestration question was settled.

Where Kubernetes Goes from Here

The central challenge facing organisations adopting Kubernetes is the migration of existing monolithic applications. These are large, tightly coupled systems whose internal logic is opaque to all but their original authors. There are three broad strategies for dealing with them:

• Full decomposition: Rebuild the monolith as a network of microservices that replicate its behaviour. High fidelity, high effort.
• API wrapping: Place an API façade around the monolith and distribute that API through a microservices-friendly network. Lower effort, but the monolith’s internal complexity remains.
• Kubernetes extension via CRDs: Rather than forcing existing workloads to conform to Kubernetes, extend Kubernetes to meet the workloads where they are. Custom controllers and operators can automate the management of workloads that predate the container era entirely.

This third path — Kubernetes adapting to existing workloads rather than the other way around — may prove to be the most consequential long-term contribution of the CRD mechanism. With hundreds of active contributors and broad commercial backing, the Kubernetes project has the capacity to build or adapt controllers for almost any class of workload an enterprise runs.

The original Kubernetes creators predicted that their platform would eventually become so embedded in data centre operations that it would be unremarkable — infrastructure, like TCP/IP. That day has not yet arrived. Kubernetes is still being actively shaped, and decisions made in its project today will define how distributed computing operates for the next decade.

What Is DevOps, Really?

At its most fundamental level, DevOps is a process — and a cultural shift. Historically, development and operations teams worked in separate silos, each with its own priorities, tooling, and communication patterns. DevOps dismantles those silos by enabling direct collaboration between the two groups, accelerating delivery and improving the accuracy of software releases.

But DevOps is broader than a workflow change. It is, as contributor Bill Kleyman has noted, a “cultural shift in how processes, code, and technology are delivered.” Critically, DevOps is not exclusively for developers. Operations engineers, QA teams, security professionals, and even business leaders all have a stake in a successful DevOps transformation.

The sections below organize the key themes of DevOps into five practical areas: challenges, common mistakes, best practices, skills and careers, and tools.

DevOps Challenges

Adopting DevOps is rarely a smooth, linear journey. Teams encounter a predictable set of obstacles, and understanding them in advance can save significant time and frustration.

Cultural Friction

The most common barrier to DevOps adoption is not technical — it is human. Development teams are often optimized for speed and change, while operations teams are optimized for stability. Bridging that gap requires deliberate effort, executive support, and shared incentives. Teams that manage this friction consistently report positive downstream effects: faster time to market, improved productivity, and stronger customer relevance.

Cloud Adoption Accelerates the Need for DevOps

As organizations migrate workloads to cloud platforms, dynamic infrastructure becomes the norm. Cloud-native architectures naturally accommodate continuous delivery pipelines and iterative deployments — making DevOps practices not just helpful, but necessary for effective operations.

Spanning Different Philosophies and Toolsets

Integrating dev and ops sounds straightforward on a slide deck, but in practice it means reconciling different mindsets, workflows, and tools. The effort required is substantial, but organizations that push through report significant rewards in delivery speed and team cohesion.

Common Misconceptions That Stall Progress

Numerous myths surround DevOps — that it only applies to large engineering organizations, that it requires a complete toolchain replacement overnight, or that it is simply “Agile for Ops.” These misconceptions cause teams to delay adoption or implement DevOps in ways that undercut its value. Addressing them early is essential.

Common DevOps Mistakes (and How to Avoid Them)

Learning from the failures of others is one of the fastest ways to accelerate your own DevOps journey. The following patterns appear repeatedly across organizations of all sizes.

• Failing slowly instead of fast: DevOps encourages a “fail fast, learn fast” mindset. Prolonged, expensive failures that are not analyzed and corrected quickly negate the benefits of an iterative approach.
• Treating DevOps as a purely technical initiative: Technology is an enabler, not the driver. Organizations that ignore cultural and process change while focusing only on tools consistently struggle.
• Underestimating risk management requirements: DevOps is designed to reduce delivery risk, but the transition itself introduces new risks. Treating a DevOps rollout as a risk management exercise — with clear rollback plans and staged releases — improves outcomes.
• Moving too fast without organizational readiness: Before committing to continuous integration and continuous delivery (CI/CD), teams should honestly assess existing culture, process maturity, and management support. Skipping this step leads to chaotic implementations.
• Leaving business leaders out of the conversation: Some of the most damaging roadblocks to DevOps come from outside the IT group. Business unit leaders and executives need to understand, support, and actively participate in the initiative.

DevOps Best Practices

Once you have navigated the challenges and learned from common mistakes, the following practices provide a solid foundation for a sustainable DevOps program.

Embrace Continuous Innovation

DevOps is not a one-time project with a defined end date. It is an ongoing commitment to continuous improvement — in process, tooling, and team capability. Organizations that treat it as a destination rather than a journey tend to stagnate.

Start Small, Scale Deliberately

Enterprise-scale DevOps is achievable, but it rarely starts there. Successful programs typically begin with a small, dedicated team working on a well-defined product or service. Lessons learned from that initial effort inform how practices scale across the broader organization. A five-step approach — start small, build a dedicated team, establish feedback loops, choose appropriate technology patterns, and formalize what works — is a common structure used in practice.

Measure What Matters

Without clear metrics, it is impossible to know whether a DevOps initiative is working. Key measurements typically include deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate. Knowing which metrics to track — and who is accountable for acting on them — is as important as the measurement itself.

Build DevOps into Organizational Culture

DevOps practices that are grafted onto an unchanged organizational culture rarely survive long-term. The goal is to embed collaboration, shared ownership, and continuous feedback into daily working patterns — not just into sprint ceremonies or deployment pipelines.

Evaluate Cloud Migration for Your DevOps Architecture

Cloud platforms offer managed services, elastic infrastructure, and integrated CI/CD tooling that can significantly reduce the operational burden of running a DevOps program. The decision to migrate DevOps infrastructure to the cloud should weigh cost, security requirements, and the team’s existing cloud maturity.

DevOps Skills and Career Opportunities

DevOps has created a distinct and growing category of roles that blend software engineering competencies with infrastructure and operational knowledge.

Core Technical Skills

• Scripting and automation (Python, Bash, PowerShell)
• Infrastructure as Code (Terraform, Ansible, Pulumi)
• Containerization and orchestration (Docker, Kubernetes)
• CI/CD pipeline design and management
• Cloud platform proficiency (AWS, Azure, GCP)
• Monitoring, observability, and logging
• Security fundamentals (increasingly central via DevSecOps)

Mindset Matters as Much as Technical Skills

Hiring managers in the DevOps space have increasingly shifted their focus from requiring proficiency in specific tools toward evaluating how a candidate thinks and collaborates. Adaptability, comfort with ambiguity, and a willingness to share ownership across team boundaries are traits that often predict success more reliably than a particular certification or tool skill.

Addressing the Talent Gap

Organizations that hold out for rare, fully-formed DevOps engineers — sometimes called “unicorns” — often overlook strong candidates who can grow into the role. Reskilling existing IT staff with DevOps training has proven to be a viable and often faster path to building organizational capability than external hiring alone.

Empowering Developers Across Environments

Modern software engineers are expected to work across development, staging, and production environments — not just write code in isolation. This requires organizations to remove platform silos and adopt hybrid IT architectures that give developers appropriate access and context without creating security or compliance risk.

DevOps Vendors and Tools

No DevOps program operates without tooling, but tool selection should follow process and cultural clarity — not precede it. The DevOps toolchain typically spans several functional categories.

Core Tool Categories

• Version Control: Git (GitHub, GitLab, Bitbucket) is the foundation of any modern software delivery pipeline.
• CI/CD: Jenkins, GitLab CI, GitHub Actions, CircleCI, and Tekton are widely used for automating build, test, and deployment workflows.
• Configuration Management: Ansible, Chef, and Puppet automate the provisioning and management of infrastructure.
• Containerization: Docker for packaging applications; Kubernetes for orchestrating containers at scale.
• Monitoring and Observability: Prometheus, Grafana, Datadog, and the ELK Stack (Elasticsearch, Logstash, Kibana) provide visibility into running systems.
• Security (DevSecOps): Tools like Snyk, Trivy, and SAST/DAST scanners integrate security checks directly into CI/CD pipelines.

Managing Tool Proliferation

One of the persistent challenges in mature DevOps organizations is tool sprawl. Teams accumulate tools organically — often solving the same problem with different solutions across squads. This creates integration complexity, increased licensing costs, and cognitive overhead for engineers who must context-switch between systems. Periodic toolchain audits and standardization efforts are a healthy part of a maturing DevOps program.

Getting Started: Making DevOps Less Scary

The scale and scope of DevOps transformation can feel overwhelming, but the path forward does not require solving everything at once. A practical starting point:

1. Audit your current state. Honestly assess team culture, deployment frequency, incident recovery times, and cross-team communication patterns.
2. Identify one high-value, low-risk candidate. Pick a product or service where improved delivery speed would have clear business value and where the team is open to change.
3. Build shared goals. Align dev and ops teams around a common definition of success — not just technical uptime or feature velocity in isolation.
4. Instrument before you optimize. Establish baseline metrics before making changes so you can demonstrate (or disprove) improvement.
5. Iterate. Apply what you learn from the first team to the next, scaling gradually rather than attempting a big-bang transformation.

DevOps is a journey with no fixed endpoint, but every step toward greater collaboration, automation, and shared ownership delivers measurable value. The organizations that thrive are those that start, learn, and keep moving.

This guide covers the demand for DevOps skills, sample interview questions grouped by topic, and best practices for making strong hiring decisions.

Demand for DevOps Skills

As organizations continue to adopt cloud computing, the demand for professionals who can operate effectively at the intersection of development, operations, and infrastructure has grown steadily. IT engineers, software engineers, and cloud architects are among the roles seeing the most direct benefit from DevOps expertise.

Among the most sought-after technical skills for DevOps practitioners are:

• Configuration management: Experience with tools such as Puppet, Chef, and Ansible to automate infrastructure provisioning and maintain consistency across environments.
• Containerization and virtualization: Hands-on experience with platforms like Docker, Kubernetes, OpenStack, and cloud providers such as AWS, Azure, or GCP.
• Scripting and programming: Proficiency in languages commonly used in automation and tooling, including Python, Ruby, Go, Bash, and Java.
• CI/CD pipeline management: Experience designing, building, and maintaining continuous integration and continuous delivery workflows.
• Monitoring and observability: Familiarity with monitoring stacks and the ability to design alerting and incident response strategies.

Sample DevOps Interview Questions

The following questions are grouped by topic area. They are useful both for interviewers designing a structured assessment and for candidates preparing their responses.

Understanding of DevOps

What is your understanding of DevOps?

This foundational question assesses whether the candidate understands DevOps as more than a toolchain — it is a cultural and organizational approach that improves collaboration between software development and IT operations teams. A strong answer will go beyond listing tools and describe how DevOps shortens feedback loops, reduces deployment risk, and aligns engineering work with business outcomes.

Why does DevOps matter, and what problems does it solve?

This question probes the candidate’s ability to articulate the business value of DevOps. Look for answers that address both the technical benefits — such as faster, more reliable deployments and reduced mean time to recovery — and the cultural benefits, including breaking down silos between development and operations teams. Candidates who have lived through a DevOps transformation will often have concrete examples to draw on.

Agile Development

How are DevOps and Agile methodology related?

DevOps and Agile are complementary but distinct. Agile focuses on iterative software development in short cycles; DevOps extends this thinking into the deployment and operations phases. A well-prepared candidate will explain that DevOps is often seen as the natural operational companion to Agile development — both share a commitment to rapid feedback, collaboration, and continuous improvement. They should also be able to distinguish where each practice applies and how they interact in practice.

What is the role of a DevOps engineer within an Agile team?

This question uncovers whether the candidate understands their specific responsibilities in a cross-functional team. Key functions include maintaining CI/CD pipelines, enabling automated testing, managing infrastructure as code, and supporting rapid, reliable delivery of each sprint’s output. Strong candidates will also highlight the importance of acting as a bridge between developers and operations — attending standups, participating in sprint planning, and helping the team understand infrastructure constraints early.

Software Deployment

Describe your experience with software deployment pipelines.

This open-ended question lets the candidate demonstrate hands-on experience with deployment automation. Listen for familiarity with tools like Jenkins, GitLab CI, GitHub Actions, CircleCI, or ArgoCD, and whether the candidate can describe a real pipeline they have designed or maintained. The best answers will touch on how they handled environment parity, secrets management, and rollback strategies.

How have you handled a failed deployment in production?

Deployment failures are inevitable. This behavioral question assesses how the candidate responds under pressure. Look for a structured approach: detecting the failure quickly, deciding whether to roll back or hotfix, communicating clearly with stakeholders, restoring service, and conducting a blameless post-mortem to prevent recurrence. Candidates who have never experienced a production failure may not have been working at sufficient scale or ownership level.

How do you detect failures in production?

This question directly tests the candidate’s approach to monitoring and observability. A strong answer will describe a layered strategy: infrastructure metrics (CPU, memory, disk), application-level metrics (error rates, latency, throughput), log aggregation, distributed tracing, and alerting thresholds. The candidate should be able to name tools they have used — such as Prometheus, Grafana, Datadog, the ELK stack, or similar — and explain how they configured meaningful alerts rather than alert noise.

How can configuration management tools support a DevOps practice?

Configuration management tools like Ansible, Chef, Puppet, and SaltStack allow teams to define infrastructure state as code, apply it consistently across environments, and audit drift. A solid candidate will be able to compare at least two tools, describe when they would choose one over another (for example, agentless versus agent-based models), and explain how these tools integrate into a broader CI/CD pipeline or infrastructure-as-code workflow alongside tools like Terraform.

Why Asking the Right Questions Matters

Structured, topic-focused interview questions serve a practical purpose beyond filtering candidates. They allow you to:

• Map candidate strengths against specific gaps in your existing team — one candidate may be stronger on the infrastructure and operations side, while another brings deep CI/CD or development expertise.
• Objectively compare candidates using consistent criteria rather than gut feel.
• Assess depth of experience versus surface-level familiarity with buzzwords.
• Determine whether a candidate understands the full software delivery lifecycle, not just their slice of it.
• Gauge a candidate’s ability to collaborate across team boundaries — a critical soft skill in DevOps environments.

DevOps roles vary significantly between organizations. A startup may need a generalist who can own the entire pipeline, while an enterprise may need a specialist who fits into a larger platform engineering team. Tailor your questions accordingly.

Best Practices for Hiring DevOps Engineers

Effective DevOps hiring goes beyond the interview itself. Consider the following principles when building your hiring process and onboarding approach.

Create an environment where good engineers can do their best work

Hiring exceptional engineers is only half the equation. If your organization’s culture, processes, or tooling prevent those engineers from exercising their expertise, the talent advantage disappears quickly. Before worrying about hiring the best people on the market, evaluate whether your current environment supports autonomy, fast feedback, and psychological safety. Engineers who are trusted to make decisions and learn from failures will outperform those who are micromanaged regardless of their raw skill level.

Prioritize problem-solving ability and initiative over trivia

It is tempting to use technical screening questions as a proxy for competence — asking candidates to recite specific port numbers, obscure configuration syntax, or niche protocol details. Resist this temptation. Specific technical details can be looked up in seconds; what cannot be easily taught is the drive to identify a problem, design a solution, and see it through to completion. Focus your interview process on how candidates approach ambiguous problems, how they communicate during incidents, and how they learn from mistakes.

Assess cultural and collaborative fit alongside technical skill

DevOps is fundamentally about collaboration. A technically brilliant engineer who refuses to share knowledge, dismisses feedback, or works exclusively in silos can actively damage a DevOps culture. During interviews, pay attention to how candidates describe past team dynamics, how they talk about colleagues in other disciplines, and whether they show genuine curiosity about the problems your team is trying to solve.

Consider practical assessments

Where appropriate, supplement verbal interview questions with a practical exercise — such as reviewing a Dockerfile or a CI pipeline configuration and asking the candidate to identify issues and suggest improvements. Keep take-home assessments short and respectful of the candidate’s time, and always provide feedback regardless of outcome.

Additional Resources

For further reading on DevOps interviewing and hiring, the following articles on this site may be useful:

• 10 questions every DevOps candidate should answer
• Why you’ll NEVER nail that DevOps interview

Overview

F5 Networks released the results of a survey comparing the views of more than 850 NetOps and DevOps IT professionals on their disciplines, collaboration practices, and attitudes toward automation and multi-cloud deployments. The conventional wisdom has long painted NetOps and DevOps as adversarial groups with incompatible goals. The survey data tells a more nuanced story: the two groups share more common ground than the “us vs. them” narrative suggests, but specific friction points around pipeline access and skill gaps are quietly driving the fragmentation of infrastructure across multiple cloud providers.

Key Survey Findings

Mutual Respect for Each Other’s Priorities

More than three-quarters of respondents in both groups said they believe the other function is prioritizing the right things within IT. This level of cross-functional respect is significant: it signals a shared understanding of broader organisational goals and a foundation for deeper collaboration. Deployment frequency was also a point of agreement, with 70% of DevOps and 74% of NetOps respondents indicating satisfaction with the pace at which applications and services are being delivered.

Shared Support for Automation

Both groups rated pipeline automation as important, though with slightly different levels of enthusiasm. On a 5-point scale, DevOps respondents gave automation an average significance rating of 4.0, while NetOps rated it 3.5. Critically, both groups reported higher confidence in application reliability, performance, and security when the production pipeline was more than 50% automated. This is a meaningful data point: it suggests that increasing automation is not just a DevOps preference but a broadly held belief about how to build more dependable systems.

Friction Over Pipeline Access

The clearest point of divergence concerns how much access DevOps should have to production resources. Forty-five percent of DevOps respondents believe they should have access to at least 75% of the production pipeline. Only 31% of NetOps respondents agreed with granting DevOps that level of access. This 14-point gap reflects a structural tension: DevOps teams need broad access to move fast, while NetOps teams carry responsibility for security, availability, and performance — and are therefore more cautious about permissions.

Left unresolved, this misalignment creates organisational drag. Processes that should be streamlined stall, and application delivery timelines slip — directly impacting business outcomes in an environment that increasingly depends on rapid digital delivery.

Pipeline Access Gaps Are Fuelling Multi-Cloud Sprawl

The access friction has a concrete downstream effect: it is pushing DevOps teams toward cloud solutions outside the traditional IT stack. Sixty-five percent of DevOps respondents said they are influenced “a lot” or “some” toward adopting cloud solutions by the level of automation and self-service access available in the pipeline. On the NetOps side, 44% said DevOps’ use of external cloud technologies affects their willingness to grant pipeline access “some,” with an additional 21% saying it affects them “a lot.”

The result is a self-reinforcing cycle: limited pipeline access leads DevOps to adopt cloud-based workarounds, which increases NetOps’ caution about permissions, which further limits access and drives even more cloud adoption. The end state is a multi-cloud environment that evolves organically rather than by design — making application delivery, scaling, and security progressively harder to manage.

Bridging NetOps and DevOps: What the Data Suggests

The survey results collectively point to automation and self-service capabilities as the most productive area of shared investment. Both teams want more of it; both teams report better outcomes when it is in place. The challenge is agreeing on the governance model that determines who controls what — and to what extent.

Skill gaps compound the problem. DevOps respondents rated their own skills at 3.6 out of 5 in terms of meeting their job function requirements. NetOps rated themselves slightly lower at 3.4. Neither score is alarming in isolation, but in the context of rapid cloud adoption and increasing infrastructure complexity, confidence gaps on both sides can slow the adoption of the very automation tools that would benefit everyone.

Practical Implications for Engineering Teams

• Define pipeline access policies jointly. The data shows that NetOps and DevOps disagree on access levels — but both groups respect each other. This is the right environment for a structured conversation about role-based access controls (RBAC) and clear ownership boundaries within CI/CD and production pipelines.
• Treat automation as a shared goal, not a DevOps project. NetOps teams that score automation at 3.5 out of 5 are not opposed to it. Framing automation initiatives as cross-functional efforts — with NetOps involved in design and governance — is more likely to build consensus than presenting them as developer-led mandates.
• Address multi-cloud sprawl proactively. Unplanned multi-cloud adoption driven by access limitations is harder to secure and govern than planned multi-cloud architectures. Teams should assess whether current access friction is a root cause of cloud sprawl before adding more cloud providers.
• Invest in cross-functional training. Skill gaps in both groups are real. Certification programs, shared labs, and cross-team training reduce the knowledge silos that reinforce the NetOps/DevOps divide.
• Establish shared KPIs. Deployment frequency, application reliability, and security incident rates are metrics both teams care about. Shared dashboards and shared accountability help align incentives.

Context: Why This Still Matters

Although this survey was conducted in 2017, the underlying tensions it identifies have not disappeared. If anything, the proliferation of Kubernetes, service meshes, cloud-native networking, and platform engineering has made the NetOps/DevOps boundary more complex, not less. The debate over who owns the network layer in a containerised, multi-cloud environment is an active one in most large engineering organisations. The finding that both groups broadly support collaboration — but diverge on access — remains a useful diagnostic frame for teams trying to understand why their DevOps transformation is stalling.

Source

Survey data and executive commentary sourced from an F5 Networks press release published via Business Wire, August 2017. The full companion report with methodology details was referenced in the original announcement but is not linked here.

Why Certain DevOps Skills Command Premium Salaries

DevOps professionals who earn the highest salaries tend to combine deep technical expertise with strong cross-functional collaboration abilities. They understand enterprise automation inside-out, can navigate every stage of the software development lifecycle, and treat organizational change as an opportunity rather than a threat. Where most employees see disruption, a skilled DevOps engineer sees a process gap to close and a solution to propose.

Looking at the salary data, a clear pattern emerges: the highest-paying DevOps skills cluster around automation and configuration management. This makes intuitive sense. Tools like Ansible and Puppet allow engineering teams to provision, configure, and deploy infrastructure reliably at scale — whether a company runs ten servers or ten thousand. Professionals who master these platforms become force multipliers for their organizations, and the market compensates them accordingly.

Top-Paying DevOps Skills: A Breakdown

1. Ansible — $121,382

Year-over-year change: N/A

Ansible topped the salary rankings at the time of this data. An open-source automation engine written in Python, Ansible handles configuration management, application deployment, orchestration, and other IT tasks using a simple, human-readable YAML syntax called Playbooks. Unlike some competing tools, it is agentless — it communicates with managed nodes over SSH, reducing the operational overhead of keeping agent software up to date. Ansible is integrated into the Fedora Linux distribution and is available through several Red Hat product offerings. Its low barrier to entry combined with its power at scale makes Ansible expertise highly sought after.

2. KornShell — $118,273

Year-over-year change: +1.4%

KornShell (ksh) is a Unix shell with roots stretching back more than thirty years. It was developed at Bell Labs and incorporates features from both the Bourne shell (its primary source base) and the C shell, including job control, command history, and dynamic extensibility of built-in commands. The high salary associated with KornShell expertise reflects its presence in legacy enterprise and mainframe environments. Organizations running long-lived critical systems often cannot easily migrate away from ksh scripts, so specialists who understand its nuances remain valuable and relatively scarce.

3. Jenkins — $113,354

Year-over-year change: +0.5%

Jenkins is one of the most widely used open-source automation servers in the DevOps ecosystem. It is the backbone of continuous integration and continuous delivery (CI/CD) pipelines for countless organizations. Jenkins supports integration with a broad range of version control systems including Git and Mercurial, and can trigger builds from shell scripts, Apache Ant, Apache Maven, and many other tools. Its extensive plugin library — numbering in the hundreds — means it can be adapted to almost any workflow, which explains both its popularity and the sustained demand for engineers who can architect and maintain Jenkins pipelines.

4. Puppet — $112,883

Year-over-year change: -3.7%

Puppet uses a declarative, Ruby-based domain-specific language (DSL) to describe the desired state of infrastructure. Once a desired state is defined, Puppet enforces it continuously — automatically correcting configuration drift without requiring manual intervention. This makes it particularly appealing to system administrators, cloud architects, and teams responsible for large-scale virtualized environments. Puppet’s agent-based architecture means it can manage thousands of nodes consistently, and its integration with reporting and auditing tools adds governance value in regulated industries.

5. Chef — $112,523

Year-over-year change: -3.8%

Chef takes an infrastructure-as-code approach, using Ruby-based “recipes” and “cookbooks” to define server configuration and application deployment processes. Its tight integration with cloud platforms such as AWS EC2 and Microsoft Azure makes it a natural fit for hybrid and multi-cloud environments. Chef scales effectively from small startups to large enterprises, and its test-driven approach to infrastructure — supported by tools like Test Kitchen and ChefSpec — resonates with engineering cultures that prioritize code quality.

6. Vagrant — $111,422

Year-over-year change: +3.3%

Vagrant is a tool for building and managing portable, reproducible virtual development environments. Written in Ruby, it works with hypervisors such as VirtualBox, VMware, and cloud providers to spin up pre-configured development environments from a single Vagrantfile. By codifying the development environment alongside application code, Vagrant eliminates the classic “works on my machine” problem. Developers and system administrators appreciate the time savings from automated provisioning, and the tool’s 3.3% year-over-year salary growth at the time of this data suggests its adoption was still accelerating.

Key Takeaways for DevOps Career Development

• Automation skills are the highest earners. Configuration management and CI/CD tooling dominate the top of the salary list. Investing time in tools like Ansible, Puppet, Chef, or Jenkins has a measurable compensation payoff.
• Legacy expertise retains value. KornShell’s place near the top of the list is a reminder that organizations running long-lived systems pay well for specialists who can maintain them safely.
• Soft skills matter. Technical tool proficiency alone does not explain premium salaries. The ability to collaborate across teams, communicate change effectively, and guide organizations through transitions is equally important.
• The market evolves quickly. The negative year-over-year changes for Puppet and Chef hint at competitive pressure from newer tools. DevOps professionals should monitor the ecosystem continuously rather than relying on any single skill.
• These figures are historical. Salary data from 2017 reflects a specific moment in a fast-moving market. Use this data for directional insight, and consult current sources such as Stack Overflow’s annual Developer Survey, Levels.fyi, or LinkedIn Salary for up-to-date benchmarks.

The broader lesson is consistent: DevOps professionals who combine hands-on expertise in automation tooling with the organizational and communication skills to drive change at scale are the ones who command — and deserve — the highest compensation.