Introduction
The DevOps lifecycle describes the continuous chain of phases a piece of software moves through from idea to running production system and back again: Plan, Code, Build, Test, Release, Deploy, Operate, and Monitor. Unlike a traditional handoff where developers finish work and throw it over a wall to a separate operations team, DevOps treats these phases as one connected loop owned jointly by both groups, with each phase feeding data and decisions into the next.
Cricket analogy: A franchise doesn't let selectors pick a squad, hand it to coaches, and vanish; selection, training, match-day tactics, and post-match review form one continuous loop the same coaching staff owns, mirroring how DevOps keeps Plan through Monitor as one connected cycle instead of separate handoffs.
Explanation
During the Plan and Code phases, teams define requirements and write software against them, while the Build and Test phases compile that code and validate it automatically through unit, integration, and sometimes performance tests before a human ever looks at it. Automation is what makes the lifecycle move fast without becoming careless: a build that fails tests never proceeds to Release, and a release that fails an automated gate never reaches Deploy, so quality checks are pushed as early as possible rather than caught late.
Cricket analogy: A domestic academy doesn't let a batter walk straight into the first-class side after one net session; drills, simulated match scenarios, and fitness benchmarks must all pass first, just as DevOps blocks a build that fails automated tests from ever reaching Release.
The final phases, Operate and Monitor, are where the loop closes: once software is deployed, monitoring tools collect metrics on performance, errors, and usage, and that data feeds directly back into the next Plan phase, informing what gets prioritized next. This feedback loop is the defining feature of the DevOps lifecycle over older waterfall models, where operational data rarely made it back to the people planning future work in any structured way.
Cricket analogy: A team's post-series analytics on where wickets fell and which deliveries went for runs directly shape the next tour's training plan, just as DevOps monitoring data from Operate feeds straight back into the next Plan phase.
Example
# A minimal CI stage that enforces the Build -> Test -> Release gate
name: pipeline
on: [push]
jobs:
build-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npm ci # Build
- run: npm test # Test - failure stops the pipeline here
- run: npm run package # Only reached if tests pass -> feeds ReleaseKey Takeaways
- The DevOps lifecycle is a continuous loop: Plan, Code, Build, Test, Release, Deploy, Operate, Monitor.
- Automation gates each transition so failing builds never reach Release or Deploy.
- Monitoring data from the Operate phase feeds directly back into the next Plan phase, closing the loop.
- The lifecycle removes the wall between development and operations that waterfall models rely on.
Practice what you learned
1. Which phase directly follows Build in the standard DevOps lifecycle?
2. What happens when a build fails its automated tests in a DevOps pipeline?
3. What is the defining feature that separates the DevOps lifecycle from a waterfall model?
4. Which two phases together validate code before it is released?
5. In DevOps, who typically owns the full lifecycle from Plan through Monitor?
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