Guide to Continuous Delivery (CD) Pipelines

Releasing software has come a long way from the days of manual deployments and late-night releases. Today, development teams work in shorter, more predictable cycles—integrating new code continuously and delivering updates with confidence that they’ve tested and verified every change along the way. That rhythm of steady, dependable delivery is powered by the continuous delivery (CD) pipeline.

A CD pipeline is more than a sequence of automated steps. It’s a shared workflow that connects development, testing, and operations into one collaborative process. When it’s working well, teams spend less time managing deployments and more time improving the product itself. CD pipelines bring consistency to release cycles, reduce risk, and help everyone—from engineers to testers to product managers—see exactly where a project stands at any moment.

In this guide, we’ll explore what a continuous delivery pipeline is, how each phase works, the benefits and challenges of building one, and how teams can measure success using clear data and meaningful metrics.

What is a continuous delivery pipeline?

A continuous delivery (CD) pipeline is an automated, end-to-end process to help teams move code changes from development to production safely and consistently. It connects every part of the software lifecycle—building, testing, staging, and releasing—into a single, repeatable flow that keeps code in a deployable state at all times.

You can think of a CD pipeline as a digital assembly line for software delivery. Just as ETL pipelines automate how data is extracted, transformed, and loaded, CD pipelines automate how software moves through each stage of release, maintaining consistency, quality, and confidence every step of the way. These automated workflows rely on strong data integration and clear communication so every team can see progress and identify issues early.

Continuous delivery vs continuous integration vs continuous deployment 

Continuous delivery pipelines build on the foundation of continuous integration (CI)—the practice of merging code frequently and running automated tests to catch issues early. While CI focuses on preserving code quality within the development environment, CD takes the next step: It automates the path from “ready to test” to “ready to deploy.”

It’s important to note that continuous deployment slightly differs from continuous delivery. Continuous deployment automatically pushes every validated change directly into production without a person’s approval. In contrast, continuous delivery stops just short of that step, giving teams the opportunity to review and approve changes before release.

How a continuous delivery (CD) pipeline works

A continuous delivery (CD) pipeline brings together a series of connected stages that move code from development through testing and into production. Each stage validates the software in a different way, helping teams identify problems earlier, reduce risk, and deliver changes with greater confidence.

At a high level, a CD pipeline includes four main phases: component, subsystem, system, and production. Each phase of a continuous delivery pipeline builds on the last, combining automation, collaboration, and insight into one unified process. Together, they form a repeatable workflow that teams can rely on for every release.

Step 1: Component phase

The component phase is where quality begins. Individual developers or small teams review, test, and validate their own pieces of code before those components are integrated with the rest of the system.

Typical activities at this stage include:

• Code reviews to verify readability and maintainable structure.
• Unit tests to confirm that each function behaves as expected.
• Static code analysis to identify security flaws or code problems early.

Here, automation is key. Developers often use continuous integration tools to trigger automated tests whenever new code is committed. Teams also use data visualization dashboards to track test coverage, code quality scores, and issue trends in real time. By visualizing this data, developers can spot patterns—like recurring test failures or performance slowdowns—and address them quickly before they escalate.

This early phase keeps ownership close to the people writing the code, building a culture of accountability and partnership.

Step 2: Subsystem phase

Once individual components are validated, the next step is to see how they work together. The subsystem phase certifies integrated pieces of functionality in a controlled test environment.

Here, teams focus on:

• Functional testing to verify that the combined components behave correctly.
• Performance benchmarking to measure responsiveness and scalability.
• Security checks to uncover vulnerabilities or misconfigurations.

Because integration testing can surface a wide range of issues, visibility is critical. Teams rely on real-time data dashboards to monitor subsystem performance, test results, and resource usage as they happen. Continuous feedback loops help teams adjust configurations and maintain stability as systems grow more complex.

At this stage, a CD pipeline starts to resemble a feedback engine, automatically surfacing the information required to improve code quality with each iteration.

Step 3: System phase

The system phase validates the entire assembled product as a whole. Once the subsystems are stable, the full application is deployed into a staging environment that mirrors production as closely as possible. This environment lets teams simulate real-world conditions before releasing any code for use.

Testing here is comprehensive and includes:

• Integration tests to verify that all services communicate correctly.
• Load and stress tests to measure performance under peak demand.
• Security and compliance checks across network layers and interfaces.

Keeping the staging environment as close as possible to production helps prevent unexpected issues when new code goes live. That’s where strong data governance practices come in. Governance keeps configurations, security standards, and data access consistent across every environment.

Teams use analytics tools to monitor results and share what they learned in this stage. For example, visualizing response times, memory usage, or error rates helps identify patterns that may not appear in raw logs. This level of transparency allows everyone, from QA engineers to DevOps specialists, to make informed decisions grounded in data.

Step 4: Production phase

The final phase of the pipeline moves validated code into the production environment where people interact with it. This step requires precise and coordinated action to maintain uptime and avoid disruptions.

Common deployment strategies include:

• “Blue-green” deployments, where two identical environments alternate between active and idle states, allowing instant rollback if issues arise.
• “Canary” releases, which roll out updates gradually to a small subset of teams or customers before expanding to the full audience.
• “Zero-downtime” deployments, using automation to replace old code without service interruptions.

Many teams also set up manual gates—approval steps that require someone to review before final release. This safeguard is especially useful for industries with strict compliance or audit requirements.

Throughout this phase, automation handles repetitive tasks, while people focus on oversight and improvement. Integrating data automation into deployment workflows reduces traditional handoffs and helps maintain consistency across environments.

By tracking key performance metrics in real time, such as length of deployment, error rates, and feedback, teams can quickly measure the impact of each release. These metrics become actionable data that guides future decisions and creating the conditions continuous improvement after each deployment.

A continuous, data-driven cycle

A well-designed CD pipeline doesn’t end at production; it loops back into itself. Insights from monitoring and analytics feed directly into planning for the next iteration. When teams can visualize their delivery process from end to end, they gain the ability to identify bottlenecks, adjust priorities, and improve efficiency over time.

For teams, this means fewer late-night release scrambles and more time spent on meaningful development. When delivery becomes predictable, teamwork improves. Developers, testers, and product leads can focus on delivering value rather than troubleshooting last-minute issues.

Benefits of a continuous delivery (CD) pipeline

A continuous delivery (CD) pipeline allows teams to release software more frequently and with greater consistency, reducing delays and easing pressure during release cycles. Automating repetitive tasks and creating predictable workflows frees people to focus on solving complex problems rather than managing deployment details.

Increases reliability and quality

Automated testing and consistent environments mean every change is verified before it reaches the people who rely on it. CD pipelines reduce the risk of regressions and shorten the feedback loop between writing and validating code. Developers can catch issues early, while testers and operations teams can focus on more valuable validation processes instead of repeating manual checks.

Boosts team productivity and teamwork

With a shared delivery process, everyone knows where a change stands and what comes next. Transparency across stages improves how developers, QA, and operations teams communicate. When testing and deployment steps are standardized, teams spend less time troubleshooting and more time building practical improvements.

Continual learning and insight

Each phase of the pipeline generates data—including build times, error rates, and test outcomes—that teams can analyze to find patterns and refine performance. These metrics become data that teams can use to guide decisions and help them improve with each iteration. Many pipelines now include AI data analytics to forecast build stability, predict test failures, and optimize release timing.

According to McKinsey, teams that embrace continuous delivery practices build more effective feedback loops and adapt more quickly to what their customers want. The result isn’t just reliable releases, it’s a culture of learning and working together that helps teams continuously improve how they deliver value.

Challenges in building a continuous delivery pipeline

Building a continuous delivery (CD) pipeline can transform how a team works, but getting there takes time, collaboration, and sustained effort. Even experienced teams run into obstacles when shifting from traditional release cycles to fully automated workflows.

Cultural resistance

The biggest challenge often isn’t technical; it’s cultural. Some teams may hesitate to embrace automation or fear that it limits flexibility. Overcoming this means creating a culture of learning, where experiments are encouraged and mistakes are treated as data, not failure.

Competing priorities

Pipeline work sometimes takes a back seat to feature development, especially when deadlines are tight. But a reliable CD pipeline is infrastructure for new ideas. Investing in it early saves teams from production slowdowns later.

Limited resources and skills

Teams may lack the time, tooling, or expertise to build and maintain automation effectively. Successful continuous delivery depends as much on process and collaboration as on technology. Investing in training, shared ownership, and documentation helps keep momentum.

Data and environment inconsistencies

Unclear configurations or poor data management practices can cause tests to pass in staging but fail in production. Establishing strong data governance, consistent environments, and automated checks keeps each phase of the pipeline working as expected.

Continuous delivery succeeds when people, processes, and technology evolve together. Teams that build incrementally—testing, learning, and adapting—create pipelines that not only deploy code but also strengthen collaboration and trust. 

Continuous delivery pipeline best practices

Once a team has the foundation of a CD pipeline in place, the next step is to refine and strengthen it. The most effective pipelines aren’t built once; they evolve as teams learn, tools improve, and projects grow more complex.

1. Start small and iterate

Instead of automating everything at once, begin with a single service or workflow. Each small success builds trust in the process and makes it easier to expand gradually. Incremental progress creates stability without overwhelming the team.

2. Embed learning into the workflow

Treat the pipeline as a feedback loop, not just a release mechanism. Track build times, failure rates, and deployment frequency to spot trends and improve efficiency. Pair these data points with augmented analytics to identify hidden patterns or recurring issues that human reviews might miss.

3. Standardize environments and processes

Use configuration management and infrastructure-as-code tools to maintain consistency across development, testing, and production environments. It reduces unexpected behavior during deployment and makes troubleshooting less time consuming.

4. Align automation with team goals

Automating processes should simplify work, not add complexity. Revisit scripts, dashboards, and alerts regularly to make sure they support current priorities.

5. Encourage working together across roles

Strong CD pipelines grow from shared accountability. Developers, testers, and operations teams should all have visibility into the same data and the same outcomes. Regular retrospectives help teams refine workflows and strengthen trust.

Ultimately, the best pipelines reflect the teams behind them—curious, adaptable, and always improving.

Measuring success in continuous delivery pipelines

Measuring success in continuous delivery (CD) pipelines can be challenging. Each release produces thousands of data points—from build times to recovery rates—and the signal can get lost in the noise. To make sense of it all, teams should look for clear metrics and shared visibility into how well their delivery process is performing. The most effective teams measure success on both technical and departmental/organizational metrics.

Technical KPIs focus on the delivery process itself:

• Deployment frequency and lead time for changes show how quickly code moves through the pipeline.
• Change failure rate and mean time to recovery (MTTR) measure stability and resilience.
• A code quality index and stability index track trends across environments—how tests perform in staging vs production.

Organizational and departmental KPIs connect delivery outcomes to team and business impact:

• Customer satisfaction and responsiveness to feedback.
• Velocity of feature delivery and ability to meet release commitments.
• Reduced downtime and improved system availability.
• Cross-team collaboration metrics that reflect how well teams coordinate work across environments.

Because pipeline data is so detailed, visualizing it in business intelligence dashboards helps teams see patterns across test, staging, and production environments. Dashboards make it easier to compare technical performance with broader outcomes and identify where improvements matter most.

When teams can interpret their delivery data clearly, they turn performance metrics into continuous insight, and continuous delivery into continuous improvement.

Build confidence in every release with Domo

A continuous delivery (CD) pipeline brings structure, transparency, and dependability to software releases. It gives teams a clear process for testing, validating, and deploying code so that every change is intentional and understood. By continuously collecting and analyzing data from each stage, teams can see how their delivery process performs and identify exactly where they will have to make improvements.

With Domo, teams can bring that delivery data together in one place—tracking build performance, monitoring release stability, and sharing insights across development, testing, and operations.

Ready to see how data can strengthen your continuous delivery pipeline? Contact Domo to transform your pipeline metrics into actionable insight—improving collaboration, reliability, and confidence in every deployment.