DevOps Lifecycle Phases and the Tools Behind Them
The DevOps culture shows up in a set of phases. You can picture them in a loop, but you can also walk them in a straight line. Each phase has a goal, a set of practices, and tools that help. Below we dig into each step with clear examples so you can see how this plays out on real teams.
1) Plan: Set Clear Objectives, Together
The planning phase sets the table. The development team starts by turning business goals into clear application objectives. In DevOps, this is not a meeting just for devs. Operations joins early to talk about deployability, observability, capacity, and risks before code even exists. Instead of vague requirements like make it fast, teams write user stories with acceptance criteria, error budget agreements, and non-functional needs like startup time, memory limits, and compliance notes. That detail helps keep surprises out of production later.
Story time: A retail team planned a flash sale feature with only performance in mind. On launch day, their database choked because nobody planned connection pooling or cache warming. After they adopted DevOps planning, ops joined the kickoff, they set loads for peak traffic, built synthetic tests for the sale, and practiced a dry run one week before launch. The next sale day felt boring in the best way. Nothing broke, and revenue went up without panic.
- Outputs: product backlog, user stories, acceptance criteria, SLOs, and basic runbooks.
- Common tools: Jira, Azure Boards, Trello, Notion, and shared docs.
- DevOps twist: operations add run concerns early so you do not pay the price late.
2) Code: Version Control and Collaboration
Once the plan is set, coding begins. The development team works on the same codebase and keeps different versions in a shared repository. Tools like Git make this doable at any scale. You create branches, write commits, open pull requests, and merge when ready. This is called version control, and it gives you history, safety, and a way to work together without stepping on toes.
Think of Git as your time machine. If a change goes wrong, you can roll back fast. If two people edit the same file, Git tracks the diff and helps you merge without losing work. Teams often choose a branch strategy like trunk-based development for high frequency or GitFlow for release-heavy work. Each pull request gets code reviews and automated checks. This slows you down a little up front, then saves you a lot later.
Story time: A startup once committed experimental code straight to main on a Friday. The hotfix baton was passed like a relay as customers reported errors. After moving to pull requests with required checks, the same team caught an unsafe dependency bump minutes after it was proposed. A quick discussion avoided a weekend of stress. That is version control working as a safety net.
- Tools: Git, GitHub, GitLab, Bitbucket.
- Practices: small commits, clear messages, pull requests, code reviews, branch protection rules.
- Artifacts: commit history, tags, release notes, and clean merge trails.
3) Build: Make the Code Executable
After coding, you need to turn source files into something you can run. Build tools like Maven and Gradle do exactly that. They resolve dependencies, compile code, run unit tests, and package the result as a deployable artifact such as a JAR, WAR, or container image. A clean build is a gate. If it fails, the pipeline stops so broken code does not sneak forward.
Behind the scenes, these tools read configuration files that define what to pull and how to package. You can publish artifacts to a repository such as Nexus or Artifactory so the same build is used across environments. That avoids the classic it works on my machine complaint. When your build is repeatable, your deployments are calmer.
Story time: A team shipped a patch compiled locally on one developer laptop. A missing native library caused production startup to fail. They moved to a standard Gradle build in CI that created one signed artifact for all environments. The next release felt uneventful, which is exactly the goal.
- Tools: Maven, Gradle, npm, pnpm, Yarn, Poetry, pip, MSBuild.
- Artifacts: binaries, packages, container images.
- Checks: unit tests, static analysis, dependency audits.
4) Test: Find Bugs Early, Often, and Automatically
Testing kicks in right after the build. You want fast checks that run on every change and deeper checks that run on main branches. Selenium is a popular tool for browser automation, and it shines for end-to-end tests that click through user flows. But do not stop there. Layer your tests like a pyramid: lots of unit tests, fewer integration tests, and a focused set of end-to-end tests that cover happy paths and critical edge cases.
Automation saves time, but humans still matter. Manual exploratory testing finds weird issues that scripts miss. Security scans catch risky libraries. Load tests show how your system behaves under pressure. Together, these tests raise confidence so deployments are not a leap of faith.
Story time: A payments team skipped browser tests because unit coverage looked high. A minor CSS change moved the Pay button slightly off-screen on smaller laptops. Sales dipped for a day. They added Selenium tests for core flows like add to cart and checkout across common resolutions. The same bug would now be caught before it ever reaches a user.
- Tools: Selenium, Playwright, Cypress, JUnit, pytest, Jest, Postman, k6, OWASP ZAP.
- Practices: test data management, parallel runs, flaky test quarantine, test reports.
- Goal: make tests fast, trustworthy, and useful to the whole team.
5) Release and Deploy: Move to Production Safely
Once tests pass, the code is ready for deployment and gets handed to the operations side. In DevOps, that handoff is automated as much as possible. Tools like Ansible help you describe and apply server changes. Docker packages your app and its dependencies in a container so it runs the same everywhere. Kubernetes takes those containers and runs them across a cluster with scaling, self-healing, and rolling updates built in.
Safe deployment patterns make change feel routine. Blue-green deployments bring up a fresh version beside the old one, then switch traffic when checks pass. Canary releases send a small slice of users to the new version, watch metrics, then expand. Feature flags let you ship code dark and flip features on when ready. Each pattern reduces risk by giving you a quick way to roll back.
Story time: A media company used to push releases at midnight and hope for the best. When an issue hit, everyone scrambled. After moving to Docker and Kubernetes with canary rollouts, they shipped in the afternoon, watched error rates and latency on a small group, then ramped confidently. The drama disappeared. The business noticed that customers did not notice releases anymore, which is the point.
- Tools: Ansible, Docker, Kubernetes, Helm, Argo CD, Terraform for infrastructure, Vault for secrets.
- Practices: immutable artifacts, environment parity, automated rollbacks, change approvals in code.
- Signals: error rate, latency, saturation, and user impact during rollout.
6) Operate and Monitor: Watch, Learn, and Improve
After deployment, the job is not done. Monitoring keeps a pulse on the system. Nagios has long been a go-to for checks and alerts. It watches services, servers, and network health and sends alerts when thresholds are crossed. Modern stacks often add metrics, logs, and tracing so you can see not just that something broke, but why it broke and where.
Good monitoring is not about noise. It is about timely, actionable signals. You define alerts that map to user pain, not just server stats. Dashboards show trends so you can spot slow drifts before they become outages. The insights loop straight back into planning so the next sprint reflects what you learned in production.
Story time: An online course provider saw CPU spikes at odd hours. Nagios caught the events, but the alerts felt cryptic. The team added request tracing and found a nightly batch job colliding with backup windows. They shifted schedules and added a simple rate limit. Spikes vanished and support tickets dropped to near zero.
- Tools: Nagios, Prometheus, Grafana, ELK stack, OpenSearch, OpenTelemetry, Sentry.
- Practices: SLOs and error budgets, on-call rotations, blameless postmortems, capacity planning.
- Outcome: fewer surprises, faster fixes, clear learning that shapes the next plan.
7) Integrate Continuously: Jenkins and CI in Action
The feedback you collect feeds the next cycle. Jenkins often acts as the backbone that sends code through building and testing. When a change lands, Jenkins kicks off the pipeline. If tests pass, it sends the artifact forward for deployment. This is called continuous integration. It keeps the main branch healthy and stops drift across developers.
Pair CI with continuous delivery to keep software always in a deployable state. Small, frequent changes reduce risk. When a release is needed, you press a button or follow a policy and ship. The magic is not magic at all. It is a chain of small steps, each checked and recorded, so shipping becomes a habit, not a stunt.
Story time: A bank once did monthly releases that felt like moving day. After putting Jenkins pipelines in place with auto-tests and artifact promotion, they started merging daily. Deployments went from once a month to several times a day for low-risk services. Regulators liked the audit trails. Customers liked the faster fixes.
- Tools: Jenkins, GitHub Actions, GitLab CI, CircleCI, Azure Pipelines.
- Practices: commit-stage tests, build once deploy many, pipeline as code, secrets management.
- Signals: build time, queue time, failure rate, flaky test counts.