DevOps

DevOps is a way of working that merges software development (Dev) and IT operations (Ops) into one set of shared practices, so a single team builds a service, ships it, and keeps it running in production. It pairs a collaborative culture with heavy automation, most visibly the continuous integration and continuous delivery pipelines that carry a commit all the way to users.

The word came out of the first devopsdays conference, which Patrick Debois organized in Ghent, Belgium, in 2009. It was a reaction to the older arrangement in which the people who wrote the code and the people who ran the servers threw work over a wall at each other.

The shift is usually visible in the repository itself. Everything needed to build, deploy, and operate the service sits beside the application code, owned by the same team:

Text 
payments-service/
    src/payments/          the application code
    tests/                 the test suite
    Dockerfile             how the service is packaged
    .github/workflows/     how it gets built and deployed
    infra/main.tf          the infrastructure it runs on
    docs/runbook.md        what to do when it pages at 3 a.m.

How It Shows Up in Practice

A Python developer meets DevOps as a wider job description rather than as a meeting. Alongside the feature code come the Dockerfile that packages the service, the workflow file that tests and deploys it, and often the infrastructure as code that provisions what it runs on.

Docker, GitHub Actions, and Terraform are common choices for those three jobs, but the practice is older than any of them and survives swapping any one of them out.

The other half of the work starts after the deploy. The same team watches its own observability dashboards and takes the page when something breaks, and whoever handles the incident writes the postmortem afterward. Amazon’s Werner Vogels described that arrangement in a 2006 interview with ACM Queue as “you build it, you run it.”

DevOps isn’t really a job title, though it gets used as one constantly. A posting for a “DevOps engineer” usually describes someone who builds the pipelines, images, and infrastructure that other developers ship through, which is a platform role rather than the culture the word names.

How Teams Measure It

Because DevOps describes a culture, teams judge it by delivery outcomes instead of by which tools they adopted. The DevOps Research and Assessment (DORA) research program, which has surveyed practitioners since 2014, tracks a small set of metrics that weigh speed against stability. Four of them come up most often:

  • Deployment frequency: how often the team ships to production
  • Change lead time: how long a commit takes to reach production
  • Change fail rate: the share of deploys that need immediate intervention
  • Failed deployment recovery time: how long that intervention takes

DORA’s steady finding is that the two sides rise together, so the teams that deploy most often tend to be the ones that recover fastest. Speed and stability turn out not to be a trade-off.

One dial behind all four numbers is batch size. The slider below ships the same twelve changes, one of them carrying a bug, in bigger or smaller deploys:

Interactive diagram — enable JavaScript to view.

Chasing the numbers on their own is a known trap, though. Any one of them improves in isolation, by deploying trivial changes more often for instance, while the delivery process behind it stays exactly as fragile. The metrics work as a health check on the practices underneath, which start with continuous delivery.

Continuous Integration with Python: An Introduction

Tutorial

Continuous Integration With Python: An Introduction

In this Python tutorial, you'll learn the core concepts behind Continuous Integration (CI) and why they are essential for modern software engineering teams. Find out how to how set up Continuous Integration for your Python project to automatically create environments, install dependencies, and run tests.

intermediate best-practices devops testing

For additional information on related topics, take a look at the following resources:

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By Martin Breuss • Updated Oct. 6, 2026
Software Engineering Glossary Share Feedback