Deep Dive into the Different Phases of the DevOps Lifecycle

Over the last decade, DevOps has emerged as a game-changing software development methodology that enables teams to deliver value faster to users through improved collaboration, automation and rapid iteration. According to the State of DevOps Report 2021 by Puppet, elite performing teams who have adopted DevOps practices fully are able to deploy code over 300 times more frequently than their low performing peers.

However, for many organizations, successfully embracing DevOps remains a challenge due to difficulties in implementing the cultural change required. A key aspect of overcoming these hurdles is having a good understanding of the different phases comprising the DevOps lifecycle or workflow so that suitable processes can be put in place.

In this comprehensive guide, we take an in-depth look at the end-to-end DevOps methodology – spanning continuous development all the way through to operations – along with popular tools and best practices for each phase.

The DevOps Workflow – An Overview

The DevOps lifecycle is iterative in nature consisting of several interconnected phases that feed into each other.

DevOps Lifecycle Phases

Fig 1. High level view of key phases in the DevOps methodology

Some key aspects:

  • It starts with continuous development where code is written for new features
  • Through continuous integration, code changes are validated by running automated builds and tests
  • Continuous delivery and deployment principles are utilized to release changes quickly and reliably
  • The deployed applications and infrastructure are monitored proactively for continuous feedback about software health and user experience

Taken together, these phases reinforce each other allowing teams to sustain rapid innovation cycles. Let‘s look at each stage more closely.

Continuous Development

In traditional software development models like waterfall, code for planned requirements was written in long release cycles spanning months making the feedback loop between users and developers longer. The problem with this approach is that requirements can become outdated by the time code gets deployed.

Continuous development solves this by having developers work in shorter iterations of days/weeks to write code that delivers value early on rather than batching up changes towards the end.

Some best practices for this phase are:

  • Maintain a product backlog for user stories/requirements that is continuously groomed
  • Break down user stories into smaller, shippable units
  • Ensure developers check in code frequently through practices like daily commits
  • Automate build and test workflows to execute on every commit

Continuous Development Best Practices

Fig 2. Continuous Development Activities

Developer productivity is enhanced vastly through integrating the above practices as part of a continuous integration and delivery pipeline.

Some popular tools used are:

Version Control with Git
Git enables distributed teams to collaborate when developing applications by tracking code changes and updates to a shared repository. GitHub has further improved social coding by adding features like access control, code reviews, project management boards etc.

Containerization using Docker
Packaging applications using containers like Docker fosters consistency across environments and allows you to shift left on testing. Containers include all libraries, configuration and dependencies needed for the application to run.

IDEs like Visual Studio Code
Developers need full-featured IDEs that improve their productivity through features like intelligent code completion, debugging capabilities, collaboration tooling etc.

Metrics to track in this phase include:

  • Code commit frequency
  • Code review effectiveness
  • Build and test pass %

Continuous Integration

Continuous integration complements continuous development efforts by providing fast feedback on potential issues with code changes. It focuses on validating that changes being checked in frequently by developers integrate well with the broader codebase without breaking or destabilizing functionality.

The anatomy of a typical CI pipeline is shown below:

Continuous Integration Pipeline

Fig 3. Key stages in a continuous integration pipeline

When developers commit changes, the source code is:

  • Retrieved from the repository
  • Built to produce deployment artifacts
  • Validated through running automated tests
  • Deployed to sandbox environment for smoke testing
  • Code quality checks done through tools like SonarQube

Any failures at this stage are notified so that issues can be fixed immediately before more changes pile up downstream reducing integration debt.

Some key differences compared to continuous delivery are:

Continuous Integration Continuous Delivery
Validates dev code changes Focus on making sure changes are ready for production use
Happens earlier in pipeline Happens later towards release stage
Gated check before merge Manual checks and approvals done before full rollout

Tooling used includes:

CI/CD Tools

  • Jenkins, Circle CI – self hosted or SaaS services
  • Cloud vendor tools like AWS CodeBuild, Azure DevOps

Automated Testing

  • Unit testing using JUnit, PyUnit
  • Integration testing harness like Selenium

Artifact Repository

  • Nexus, JFrog for binary artifacts
  • Archiva for build outputs

Test Coverage Metrics

  • Cobertura for code coverage
  • SonarQube for technical debt ratio

Continuous Delivery and Deployment

The ability to release changes into production smoothly is key to realizing DevOps benefits. Continuous delivery practices allow teams to ensure that code changes can be deployed to users quickly in a sustainable way through higher levels of automation.

Continuous deployment goes one step further by releasing all changes to production automatically without any manual intervention/approvals after they pass a predefined set of quality gates.

Continuous Delivery Pipeline

Fig 4. Transitioning changes from lower environments to production

Some capabilities required to achieve this are:

Automated Release Process instead of manual handoffs – Tools like Jenkins, Spinnaker achieve this predictably

Infrastructure-as-Code principles to manage IT infrastructure used by applications – For instance, Terraform allows versioning and automation of network resources etc. Similarly tools like Ansible facilitate automation of OS configuration changes through code

Effective deployment strategies to minimize risks when rolling out changes – Techniques like Blue-Green, Canary help validate functionality before routing full traffic

Integrating compliance requirements like audits, change approval workflow automatically – Leveraging ServiceNow plugins for example

Monitoring capabilities to get feedback on availability, performance metrics of services once deployed – Tools like Nagios or Dynatrace enable this

Continuous delivery capability is a key metric for assessing DevOps maturity. The Accelerate State of DevOps Report found the highest performing teams are able to deploy changes a staggering 106 times more frequently than their low performing peers.

Continuous Testing

While testing is inherent to CI/CD pipelines through running automated checks, additional functional and non-functional testing is required throughout the lifecycle spanning development, deployment and production monitoring stages.

According to the World Quality Report, test automation adoption has increased from 16% in 2017 to over 26% in 2021 highlighting its growing importance.

Test Automation Pyramid

Fig 5. Test Automation Pyramid showing distribution for faster feedback

Some key aspects of continuous testing are:

Shift left on testing through practices like shift left security testing even before code is merged – Done through static code analysis using SonarQube or through dynamic application security testing tools

Test automation pyramid principles to categorize tests into unit, integration, UI and plan test suite accordingly for faster feedback

Incorporating both functional and non functional testing – Example performance testing with Jmeter, security validation using Fortify etc.

Effective test data management through test data generators and tools like Test Data Manager (TDM) for data masking

Mocking out dependencies to enable more robust integration testing – Libraries like WireMock, Mountebank help here

Contract testing for testing microservices integration by validating if inter-service communication conforms to expected formats without needing actual services up and running

Overall, continuous testing enables early detection of issues and reduces escape defects while also providing insights about production issues.

Continuous Monitoring

In traditional software development models, once features were deemed completed from a functional testing perspective, they were deployed to customers. However, the job doesn‘t end there – proactive monitoring is needed to ensure software works smoothly in the real world and continues to deliver value.

Continuous monitoring provides this mechanism for production oversight by enabling deep visibility into deployed services. It helps address questions like:

  • Is the service available and performing well from an end user perspective?
  • How does the software interact with supporting infrastructure elements like servers, networks?
  • Are there errors, failures or anomalies indicating problems?

This requires collecting various performance metrics spanning application, infrastructure and network layers – example:

Application Performance Metrics

  • Request response times, throughput
  • Error rates like HTTP 5xx
  • Performance near thresholds

Infrastructure Layer Metrics

  • CPU, memory utilization metrics
  • Disk or network I/O bottlenecks
  • Node/process failures

Monitoring Metrics

Fig 6. Sample metrics across application and infrastructure stack

Teams also need access to various logs, events and traces to perform complex correlations and diagnose hard-to-reproduce runtime issues through platforms like the ELK stack.

Based on the monitoring data, intelligent alerting mechanisms are configured to notify teams proactively when metrics breach certain limits before problems cascade allowing them to take timely remedial measures.

Popular tools that enable continuous monitoring at scale are:

  • Grafana for metrics visualization
  • Nagios, NewRelic provide application performance management (APM) capablities
  • Splunk offers log analytics abilities
  • Dynatrace provides end to end topology awareness through distributed tracing

Continuous Feedback

The ability to constantly collect feedback quickly and course correct application capabilities based on that is a hallmark of customer-focused product teams.

Some ways Continuous user feedback is enabled in DevOps contexts are:

  • In-app surveys help collect user opinions seamlessly at critical points
  • Product analytics through tools like Mixpanel analyze usage patterns to identify most loved/confusing features which shape roadmap
  • Beta testing done by releasing features early to select customers allows gathering feedback for enhancement before full feature launch
  • Monitoring ratings across app stores and comments on social media using tools like Hootsuite provides macro user sentiment analysis

Qualitative user feedback is complementary to quantitative metrics gathered through monitoring. Together, they help paint a complete picture.

Other cultural practices like organizing usability testing sessions, holding regular focus group discussions and fostering strong product team-customer partnerships also facilitate this.


  • The DevOps lifecycle provides a continuous integration between development and operations activities spanning multiple phases
  • Practices across continuous development, testing, monitoring and feedback gathering reinforce each other allowing faster delivery of value
  • Automation and tool adoption across the workflow is key for achieving outcomes predictabily and at scale
  • Each phase generates metrics that provide visibility enabling teams to measure outcomes
  • Taking an incremental approach instead of wholesale lifecycle adoption allows customization as per use case

Overall, the iterative nature of DevOps calibrated by measuring business outcomes accelerates app delivery tremendously while giving teams room to innovate constantly.