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DevOps Pipeline


Definition: What Is a DevOps Pipeline?

A DevOps pipeline is a collection of automated procedures and tools that enables developers (Dev) and operations specialists (Ops) to collaborate on developing and deploying code in a production environment. It is mainly used to ensure that code flows smoothly from one step to the next, enabling uninterrupted delivery, automating processes, and reducing manual labor.

A distinct feature of a DevOps pipeline is the continuous flow. Each function runs continuously instead of being subjected to one-off tests or planned deployments.

Components of a DevOps Pipeline

Organizations can utilize various techniques and instruments to build a tailored DevOps pipeline but there is no standard pipeline that applies to all organizations. It is affected by the company’s technology stack and its engineers’ abilities, including the budget and timeline.

Nevertheless, there are a few components that will always be there:

Integration (CI)

The first component of any pipeline starts with integration. The integration process is a combination of Code Integration, Continuous Testing, and Build processes. Its main goal is to automate code organization, testing, and packaging/building, increasing the efficiency of business processes.

Once a developer writes a code, they must integrate their code with a shared repository. This process makes it easier to merge code changes and identify bugs. Specifically, it helps to reduce the number of conflicts due to the delays in code changes submitted by multiple developers.

After the code is merged, the second step is to verify the usability of the code by running automated tests like unit tests. The main idea behind automating tests is to detect issues beforehand and to notify the developers if bugs are detected.

Finally, if the code passes the test run, then the next step is packaging the dependencies with the code and building them. Build process ensures that developers receive a notification if the build process is interrupted, letting them know which code lines need to be revised.

Continuous Integration process ensures that the code written by developers is continuously integrated with the releasable solution by testing and building the code.

Delivery (CD)

At the heart of every code change pushed to a repository is the delivery process. Developers write the code or build the solution so that the solution can be shared with the end user.

Continuous Delivery ensures the tested and built solution is always in a state that can be released to the end user, whenever required. By that, you might think Continous Delivery is an extension of the Continous Integration and rightfully so.

Continuous Delivery is about setting up environments that are identical to the production environment so that factors that might cause issues or failure are detected before the code changes are available to the end user. Continous Delivery component helps developers to perform additional tests such as UI tests and DevOps team to deliver bug fixes quickly, reducing the overall time and cost of a project.

Version Control

Version control is a key component of the DevOps pipeline, as it enables developers to track changes to their codebase over time and collaborate effectively. Version control systems (VCS) such as Git and Subversion allow developers to keep track of changes to their code, share their work with others, and roll back to earlier versions if necessary.

In a DevOps pipeline, version control is typically used to manage source code, configuration files, and other artifacts. Developers commit changes to the codebase to a central repository, which can then be built, tested, and deployed automatically. This allows for continuous integration and delivery, where changes are tested and deployed rapidly and frequently.

In addition to providing a central location for code storage, version control also enables developers to work collaboratively on code by allowing multiple developers to work on the same codebase at the same time. This can be particularly useful for distributed teams or teams working on large projects.

Furthermore, version control can also aid in auditing and compliance efforts. By keeping a record of all changes made to the codebase, version control can provide an audit trail that can be used to demonstrate compliance with regulations or industry standards.

Artifact Repository

An artifact repository stores and manages artifacts such as libraries, configuration files, Docker images, and other components required for the application to run.

In a DevOps pipeline, an artifact repository acts as a central location for storing and sharing these artifacts, allowing teams to easily manage and deploy their applications. It also allows for artifact versioning and management. This means that developers can keep track of changes to their artifacts over time, and easily roll back to earlier versions if necessary.

Artifact repositories can also improve the security of the pipeline, as they provide a controlled and auditable way of managing artifacts. Access to the repository can be restricted to authorized personnel, and audit trails can be used to track who has accessed or modified artifacts.

Testing Framework

Continuous testing is a key mechanism for enabling constant feedback. Changes in a DevOps process are continuously moved from integration to delivery, resulting in faster releases and better products overall. This necessitates that the product being deployed is free of bugs.

Continuous testing uses automation test frameworks to identify errors and ensure the product is free of bugs. Automated tests, such as unit tests that are run after each build modification, as well as end-to-end, functional, and smoke tests, are put in place to certify the product’s quality and notify the developers on time if issues are discovered.


Continuous operations is the approach that helps ensure the availability of apps and environments. Its goal is to avoid customers being delayed by constant code updates and maintenance work such as patches or bug fixes.

Ensuring continuous operations requires investing in a strong automation architecture that can constantly track and alert on the performance of your servers, containers, databases, networks, and applications.

Monitoring and Logging

Monitoring and logging are critical components of the DevOps pipeline that help teams to identify and address issues in their applications and infrastructure.

Using monitoring tools, teams can collect and analyze data about an application or system to detect and diagnose issues in real-time. Monitoring tools typically capture data about system performance, application behavior, and user activity, and provide alerts when issues are detected. These tools can also provide dashboards and reports to help teams visualize and understand the data.

Logging, on the other hand, involves capturing and storing data about events and transactions that occur within an application or system. This data can include error messages, user actions, and other system events. Logging tools can be used to capture and store this data, and then analyze it to diagnose issues and identify opportunities for improvement.

Monitoring and logging allows teams to identify areas of the application or system causing performance issues, which can be used to optimize application performance.

DevOps Pipeline Stages

There are no hard and fast guidelines for structuring a DevOps pipeline. DevOps teams may add or eliminate specific steps depending on their unique workflows. However, practically every pipeline has a few fundamental stages: plan, develop, build, test, deploy, and monitor.


Usually, before developers begin writing code, the complete workflow must be planned. The product team and project managers are crucial at this point. Their task is to produce a development roadmap that will direct the entire team through the procedure.

After collecting input and pertinent data from users and stakeholders, they divide the entire product into several tasks. The idea behind this granularity is teams can provide results more quickly, address problems right away, and accommodate last-minute modifications more easily by breaking the project down into smaller, more manageable pieces.


In the development phase, developers begin writing code. They install the proper IDEs, compilers, and other technologies on their local PCs to achieve optimal productivity.

Most of the time, developers must adhere to a set of coding standards and styles to achieve a consistent coding pattern so that any team member can easily comprehend and read the code.

Developers submit a pull request to the common source code repository when they are prepared to contribute their code. Once the original pull request has been approved, team members can manually evaluate the newly contributed code and integrate it into the main branch.


The build phase of a DevOps pipeline is essential because it enables developers to find code mistakes before they spread farther along the pipeline and cause problems.

Developers execute a set of automated unit tests when the recently created code has been integrated with the common code repository. Typically, a pull request starts an automatic system that packages the code into an executable or deployable package called a build.

The build fails if there is a bug in the code and the programmer is informed of the issue. The original pull request likewise fails if that does happen.

To make sure that only error-free code moves forward in the pipeline, developers go through this procedure each time they upload a file to the shared repository.


Testing begins after a successful build. In this stage, engineers execute both manual and automated tests to confirm the code’s integrity.

Acceptance testing is typically carried out. Before deploying the code to production, testers engage with the app as an end user would see if the code meets the acceptance criteria or if any more changes are required. Testing for security, efficiency, and load is also frequent at this level.


The program is prepared to be sent to production after the build has reached the deployment step of the DevOps pipeline. An automated deployment technique is used if only minimal modifications are required to the code. However, if the program has undergone a major revamp, the build is initially deployed to a setting resembling production to observe how the recently introduced code will function.

When launching substantial upgrades, a blue-green deployment technique is frequently used, where two identical environments are set up – one with an older application version and another with a newer version. Once the engineers have confirmed that the newer version of the application is performing as expected, they can direct all queries to the proper servers to make the updates available to users.

In case issues have been identified, developers can quickly switch back to the prior production environment, minimizing downtime and providing ample room for quickly addressing the issues.

How to Build a DevOps Pipeline

Businesses create distinctive and efficient DevOps pipelines that are tailored to the requirements of their organization using a variety of technologies and techniques. However, all businesses follow the procedure below to achieve a typical DevOps pipeline.

Implement a CI/CD Tool

The first step for businesses beginning to establish a DevOps pipeline is to select a CI/CD tool. There are various available on the market, free, open-source or paid, to suit different business needs; GitLab and Microsoft Azure are some of the most popular. The steps below will use Gitlab as the preferred CI/CD tool.

Set up a Repository

Create and name a new project in GitLab. Then, select a blank repository to start and create a new development branch. Name the branch and select the branch you want to base it on, such as a master branch. Once the development branch is created, you can start writing the code, making changes, and collaborating with the team members on your project.

Source a Control Environment

Large development teams require a dedicated space to store and distribute their constantly changing code, prevent merge conflicts, and quickly produce new versions of their application. Source control management solutions enable efficient teamwork in organizations where members are spread throughout the world. These solutions allow each developer’s code to be stored in a unique shared repository.

If your CI/CD tool is GitLab, it comes prebuilt with a source control environment. So, creating a project in GitLab will automatically create a source control environment for you.

Set up a GitLab CI/CD pipeline

Following the GitLab example, to configure CI/CD pipeline you will have to go to the project’s settings and select CI/CD. You can then enable CI/CD by toggling the switch to On. This will automatically create a basic .gitlab-ci.yml file in the root directory of your repository. Within the file, you must create stages and jobs to define your pipeline. The steps below will walk you through setting up testing, building, and deploying jobs.

Configure Testing Automation

After the application has been built, you need to test the code. By using automated test frameworks, developers can test their build to ensure that only error-free content moves on to the deployment step. At the testing stage, a number of automated tests are run, including integration unit, functional, and regression tests. The majority of tests are run sequentially through CI. A simple configuration file that includes testing configuration would look like this:

  # define the stage of the pipeline using stage keyword.
  stage: test
  # defining the python image to load
  image: python: 3.9-slim-buster

  # before_script runs before the tests are run
  # they are useful for installing dependencies
  # in this case we are updating our package to install latest package
  # then we are installing make command
    - apt-get install make

  # the automated test script to run
    - make test

Launch in Staging

After the code is tested, they need to be verified in a staging environment before sending to customers. Setting up your build server to launch a script to distribute your application is the simplest way to publish the code to a staging environment. You can set this to occur automatically through the CI/CD pipeline or manually through human intervention.

If we were to follow the GitLab example as shown above, the configuration file will need additional jobs for building and deploying the solution before it can be launched. After adding the jobs, the configuration file would look like:

# name of the pipeline stages
    - test
    - build
    - deploy

  # define the stage of the pipeline using stage keyword.
  stage: build

  # builds the ruby project files
    - rake


  # define the stage of the pipeline using stage keyword.
  stage: test

  # the automated ruby test script to run
    - rake test


  # define the stage of the pipeline using stage keyword.
  stage: deploy

  # the address of the deploy environment
     name: staging
     url: {url_of_the_deployment_environment}
    - echo “Run your deploy script here”
  # specify the environment to deploy using deploy keyword
  environment: production

Set Up a Monitoring Process

After your application has been deployed, you need to confirm that the application is able to solve the problems it was initially designed for. In addition, you also need to observe that the application is functioning well. A robust monitoring plan, supported by monitoring tools, helps to track any performance issues and provide timely feedback to the engineers before your customers are impacted.

If you are ready to implement your DevOps pipeline, check out our comparison of the best DevOps tools available for every stage of the process.

Merge To Main Branch

Once you have fully tested and reviewed the changes and resolved any conflicts, you can merge the changes in the development branch to the main branch. To merge changes using Gitlab, go to the merge request page for the development branch, review changes and then click the Merge button. This action will merge changes to the main branch and trigger a pipeline build (if any) for the main branch and deploy the changes to production.

DevOps Pipeline in Sematext

Sematext Cloud is a suite of performance monitoring solutions that provide real-time visiblity into your applications, infrastructure, and user experience. It offers a range of features that can help streamline and optimize your DevOps pipeline, including monitoring and logging.

For monitoring, the tool offers Sematext Monitoring, which allows you to collect and analyze critical metrics from your applications and infrastructure. It enables you to quickly identify and diagnose issues, reducing downtime and improving system performance.

For logging, you have Sematext Logs, a centralized log management solution that can help you troubleshoot problems and identify complex issues that may be difficult to detect using metrics alone.

These tools come with a powerful alerting engine which enables you to set up alerts and receive notifications whenever thresholds are reached. That way you can proactively respond to issues before they become critical, improving the overall reliability of your systems.

You can use these tools to gather logs and metrics from various data sources across your environment. Sematext offers support for a wide range of integrations, including for popular CI/CD tools such as Jenkins, so that you can seamlessly incorporate monitoring into your DevOps pipeline.

Overall, Sematext provides a powerful set of solutions to optimize your DevOps pipeline and improve the performance, reliability and efficiency of your systems.

Watch the video below to learn about how Sematext Cloud can help you. Or sign-up for the 14-day free trial to test it out.

Frecuently Asked Questions

How can I ensure traceability and visibility in a DevOps pipeline?

Logging, monitoring, and analytics tools are essential for ensuring traceability and visibility into the various stages of the pipeline. These tools help identify bottlenecks, track changes, and monitor the performance of applications in real-time.

What are the benefits of implementing a DevOps pipeline?

Some common benefits include faster and more reliable software releases, increased collaboration between development and operations teams, improved code quality through automated testing, and enhanced overall efficiency.

How can I scale my DevOps pipeline for large projects or teams?

For larger projects or teams, consider using scalable infrastructure and distributed systems. Containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes) can help manage and scale applications effectively. Additionally, adopting a microservices architecture can contribute to scalability and flexibility.

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