How to Build a DevOps Pipeline? A Complete Guide

DevOps has transformed how organizations build and ship software applications. The main objective is to provide high-quality software quickly and efficiently to end users and customers.

To achieve this, many organizations implement automated testing in the SDLC to speed up delivery times. When this automation is paired with the right tools and processes, it is known as a DevOps pipeline.

This blog provides insights into leveraging the DevOps pipeline to enhance your organization’s capabilities through efficient code compilation, testing, deployment, and delivery!

What is a DevOps Pipeline?

DevOps is a software development process that combines development (Dev) and operations (Ops). It intends to shorten the development life cycle while delivering features, fixes, and updates frequently in close alignment with business objectives.

On the other hand, a DevOps pipeline constitutes the procedural framework software engineers use to conceive, develop, assess, deploy, and perpetually enhance a software product across its development life cycle. This method entails the integration of individuals, tools, and DevOps automation mechanisms to facilitate the delivery of quality software applications.

The DevOps pipeline consists of multiple stages where development and operations professionals collaboratively prepare code for end-user dissemination. These stages include planning, coding, building, testing, deployment, monitoring, and feedback.

After the code is pushed, Continuous Integration and Continuous Delivery (CI/CD) processes ensure automated testing and seamless deployment, fostering a faster feedback loop in the SDLC. Every new code push triggers a new regression cycle that allows for efficient updates, quicker bug resolution, and enhanced overall software quality.

Why Implement DevOps Pipelines?

DevOps pipelines maintain a robust and adaptable workflow, ensuring consistent deliverables across various stakeholders involved in the Software Development Life Cycle. Automation integrated throughout the pipeline facilitates frequent delivery of high-quality software, enhancing efficiency and minimizing error risks.

Here are some of the benefits of using DevOps pipelines:

• Boosts Speed: Automation enables developers to integrate and deploy code changes more frequently, often multiple times daily. This rapid pace accelerates the delivery of new features to users, enhancing the overall speed of the development process.
• Improves Quality: Integrating testing at every stage of the pipeline catches bugs early in the development process, preventing them from reaching production. DevOps pipelines also enforce code quality standards, ensuring higher software quality.
• Seamless Collaboration: DevOps pipelines offer transparency by making the development process and application status visible. This visibility fosters collaboration between development and operations teams, creating a more cohesive and efficient working environment.
• Reduces Costs: DevOps pipelines eliminate many manual integration and deployment processes that are error-prone and time-consuming. Running automated tests in a pipeline is more cost-effective compared to manual testing, contributing to overall cost savings in the SDLC.
• Customer Satisfaction: The combination of rapid and reliable releases leads to quicker feature delivery and bug fixes. This, in turn, enhances customer satisfaction and loyalty by providing a more responsive and stable software experience.

Note

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Key Components of a DevOps Pipeline

A successful DevOps pipeline is a critical component in achieving this goal, consolidating the work of various teams to streamline software development. At its core lie the seven key components that form the foundation of a DevOps pipeline – Continuous Integration, Continuous Delivery, Continuous Testing (CT), deployment, monitoring, feedback, and operations.

• Continuous Integration and Continuous Delivery (CI/CD): Continuous Integration leads by streamlining code integration. By automating merging code changes from multiple developers and ensuring compatibility, CI reduces integration issues and accelerates the development cycle.

Whereas Continuous Deployment refers to the automated process of deploying the tested and integrated code changes directly to the production environment, seamlessly moving code from development to release. CD further accelerates the delivery of new features and improvements to users.

AI can also be integrated into Continuous Integration and Continuous Delivery pipelines in two critical ways:

• Test Automation in CI/CD: AI algorithms analyze incoming code changes, assessing their potential impact and risk. This enables predictive testing, where AI selects and prioritizes relevant test cases to improve pipeline efficiency. AI models can also forecast deployment success or failure, allowing teams to take proactive measures to mitigate issues before they occur.
• Predictive Modeling for Deployment: By training on historical data, AI can predict the likelihood of deployment success and identify potential problems, such as compatibility issues or resource constraints. This foresight allows teams to take preemptive actions to address these concerns and improve deployment reliability.

How can AI be integrated in Continuous Integration and Delivery (CI/CD)?

Let’s explore two critical aspects of AI integration in CI/CD.

1️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗜/𝗖𝗗: 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗖𝗼𝗱𝗲 𝗖𝗵𝗮𝗻𝗴𝗲𝘀

AI-driven automation in CI/CD…

— Amrut Patil🚢 (@realamrutpatil) April 2, 2024

• Continuous Testing: It is a crucial component of a DevOps pipeline that enables automated testing at every stage of development. By seamlessly integrating into the development process, continuous testing in DevOps provides swift risk assessments for code integrations, ensuring that software is thoroughly tested and meets the highest quality standards before deployment.

Similarly, organizations that actively incorporate user feedback during software development are 1.6 times more likely to meet or exceed their business objectives. CT accelerates feedback loops, enabling rapid detection and resolution of issues throughout the development life cycle.

For example, users can now provide instant feedback on new features, usability, and any encountered issues on any website through in-app feedback forms. This real-time feedback allows the development team to swiftly identify and prioritize bug fixes, leading to faster resolution of user-reported issues and a more polished product.

This proactive approach to testing improves code quality and significantly reduces the risk of encountering issues in later stages of development.

• Continuous Deployment: Continuous Deployment follows Continuous Delivery and automatically deploys updates into production if they successfully pass the automated testing phase. While this expedites deployments, there’s a risk of undetected bugs reaching the end users, potentially causing application failures.

This component of the DevOps pipeline focuses on expediting the process of rolling out updates. By leveraging modern deployment techniques such as blue-green deployment or canary releases, organizations can maximize uptime and minimize disruptions during the release phase.

• Continuous Monitoring: Continuous Monitoring ensures stability, providing real-time insights into system health and performance. Organizations can proactively detect and resolve potential issues by leveraging monitoring tools and collecting metrics on key performance indicators, ensuring smooth operation.
• Continuous Feedback: Once software is deployed, continuous feedback becomes the bridge between developers, stakeholders, and customers, gathering real-time insights into user reactions. This vital component is crucial in aligning expectations and identifying critical issues identified by testers and end users.

The DevOps pipeline has a foolproof rescue rollback mechanism if a faulty change is committed. The rollback mechanism in a DevOps pipeline ensures swift reversion to a stable state upon deployment failures, minimizing disruptions and maintaining production stability.

• Continuous Operations: Continuous Operations is vital in maintaining uninterrupted service and minimizing planned downtime for end-users. This approach emphasizes the importance of ensuring Continuous Operations by mitigating disruptions caused by hardware or software changes, thereby enhancing the reliability and availability of services.

While it may require an initial investment, the benefits of Continuous Operations far outweigh the costs, as it prevents costly production losses and keeps services running smoothly. Hence, the investment is crucial for ensuring smooth transitions and maintaining service reliability, ultimately enhancing user satisfaction.

Organizations can utilize various tools and practices to achieve Continuous Operations. For instance, implementing cloud-based infrastructure and automated deployment strategies can help reduce downtime during software updates.

Tools like Kubernetes and Docker enable seamless containerization and orchestration, allowing for efficient management and scalability of applications.

Stages of a DevOps Pipeline

In software development, there are three key environments:

• Development: It serves as a workspace for developers to write, test, and debug code while simulating production conditions.
• Testing: It closely mirrors the production environment and is used for thorough testing of the application’s functionality, performance, security, and user acceptance before deployment.
• Production: It is the live, customer-facing environment where the final, thoroughly tested application code is deployed and runs, allowing end-users to access and utilize the software application in real-world conditions.

However, a challenge that developers and testers come across is the inconsistency across deployment environments. Disparities between development, testing, and production environments, whether subtle or significant, often culminate in unforeseen issues during the deployment phase, like test failures and unstable deployments.

According to one of the statistics by Oracle, 48% of developers reported using containerization technologies like Docker during the coding and build phases of their DevOps pipelines, enabling consistent and reproducible environments across different stages.

By implementing a robust DevOps pipeline, experienced teams can ensure consistent and repeatable deployment processes, mitigating risks by implementing a robust DevOps pipeline and fostering reliability.

Development Stage

During the development stage, the team plans, codes, and introduces enhancements while addressing errors and ensuring functionality. Various tools streamline this process, making it more efficient and effective.

It involves the following stages:

1. Plan: The DevOps journey starts with collaborative planning, uniting stakeholders from the project’s inception. This phase establishes the project’s foundation. It involves mapping the workflow, breaking down the development roadmap, and structuring tasks before coding begins.



In the planning stage, you can leverage project management tools like Jira, Azure DevOps, or Asana to record and track the product roadmap. It helps plan sprints and allocate tasks from the backlog for team development, ensuring a strategic and organized kick-off to the DevOps journey.

2. Code: In the coding stage, the development team writes code and tracks progress with version control tools like Git or any other Version Control System (VCS).



DevSecOps pipelines, discussed later in this blog, use various tools for maintaining consistent code styles and identifying security issues, both static and dynamic. Cloud-hosted IDEs enhance uniformity, readability, faster debugging, and speed up coding environment creation. This stage establishes standardized code practices for efficient development. Also, adopting a cloud-based IDE ensures a consistent development environment across distributed teams, simplifying onboarding and accelerating productivity for new hires.

By leveraging version control, static analysis, cloud-based IDEs, and CI/CD integration, the coding phase has become more efficient, collaborative, and secure, delivering high-quality code consistently.

3. Build: The build stage in a DevOps pipeline plays a crucial role in ensuring code quality and reliability. When a developer commits code changes, a code review is triggered through a manual pull request, and an automated build process is initiated concurrently. This process involves merging the codebase and executing integration, unit, and regression tests.

To further enhance the code quality before deployment, it is important to incorporate cross-browser tests within the pipeline. This ensures that the application performs well across different browsers and devices. Integrating with third-party tools and services is crucial for streamlining the DevOps pipeline.

4. Test: In the DevOps approach, automation testing is vital to ensure product quality with minimal human intervention. It leverages various frameworks and tools to detect bugs, reduce errors, and improve product reliability. This enhances the product’s quality and accelerates the development process, resulting in faster product launches.

The foundation of automation testing is laid by unit tests, initiated through Test-Driven Development methodologies using tools like JUnit.

While Test-Driven Development remains a valuable practice, advanced methodologies like Acceptance Test-Driven Development (ATDD) and Behavior-Driven Development (BDD) offer a more comprehensive approach to testing. For more information on these approaches, refer to our TDD vs BDD vs ATDD.

JavaScript testing frameworks like Cucumber enable the implementation of ATDD/BDD by defining acceptance criteria and expected behaviors upfront and involving stakeholders early. Integrating such frameworks with CI/CD tools, cross-browser testing platforms, issue trackers, and collaboration platforms streamlines the entire pipeline.

Subscribe to the LambdaTest YouTube Channel and stay updated with the latest tutorials on Selenium and more.

Operation Stage

The operations phase in the DevOps lifecycle is where the software is finally released, placed appropriately, and monitored for optimal performance to ensure that end-users have a seamless experience. Despite the inherent complexity of this stage, it is a critical component of delivering updates and projects to end-users. Tools such as Nagios, Zabbix, Datadog, and Prometheus are commonly used in the operations phase to monitor resource utilization, response times, and uptime metrics.

It involves the following stages:

1. Release: In a DevOps pipeline, the release stage is critical, signifying the deployment of a thoroughly tested build. Operational performance is validated, instilling confidence in smooth live operations. Organizations vary in release approaches, including continuous deployment, manual release with final approval, or scheduled automated releases.

CI/CD platforms and DevOps tools, such as Jenkins, GitLab, CircleCI, Travis CI, and GitHub Actions, offer flexibility in tailoring release schedules and incorporating automation.

2. Deploy: In a DevOps pipeline, the release stage is designed to automate the deployment process using tools like Infrastructure as Code (IaC) or methods such as blue-green deployment. This phase allows teams to safely and efficiently roll out code changes to the live environment, ensuring minimal disruptions and maximizing user satisfaction.

One approach commonly used in the CI/CD testing for the release stage is blue-green deployment. With blue-green deployment, teams create duplicate production environments, often referred to as the blue and green environments.

IaC not only ensures that the infrastructure is in a desired state but also provides the ability to roll back to previous configurations if needed. This flexibility and version control provides an added layer of safety during the release process.

3. Operate: After successfully deploying a product through the DevOps pipeline, the operational phase focuses on maintaining seamless operations. This stage involves efficiently managing the infrastructure, making necessary real-time adjustments to optimize resource utilization, and continuously monitoring user interactions through behavioral logs and feedback forms.

The operations team plays a crucial role in configuring and overseeing the live environment of the DevOps project. They rely on automation tools and processes to ensure the deployed product runs smoothly and meets the desired performance standards.

4. Monitor: In the final stage of a DevOps pipeline, automated DevOps monitoring tools identify performance issues, application glitches, and overall application performance.

DevOps monitoring tracks vital metrics like deployment frequency, code errors, pull request cycle time, failure rates, MTTD, MTTM, and MTTR. These insights allow proactive issue resolution in real-time, optimizing the DevOps toolchain and identifying automation opportunities.

Specific tools like Splunk, Prometheus, Grafana, and Datadog collect data on application and infrastructure performance in real time, providing insights for product teams to discover bottlenecks and address problems related to app productivity.

How to Build an Effective DevOps Pipeline?

Establishing a DevOps pipeline is paramount for organizations seeking to optimize their Software Development Life Cycle. This comprises five essential steps:

1. Select a CI/CD Tool: Choose a robust CI/CD tool aligned with the project’s requirements. Popular choices like Jenkins, GitLab, TeamCity, or Bamboo offer versatile solutions. Jenkins, renowned for its open-source nature and remarkable customizability, emerges as an excellent option, enabling tailored adaptations to specific organizational needs.
2. Establish a Source Control Environment: Development teams necessitate a centralized repository for code storage and collaborative work. Git, GitLab, or BitBucket serve as effective control environments, facilitating version management and conflict resolution. These platforms enable multiple developers to collaborate seamlessly on the same codebase while avoiding merge conflicts.
3. Set Up a Build Server (CI Server): A dependable build server is critical for integrating code changes and ensuring code integrity. Jenkins, Travis-CI, or TeamCity are centralized environments retrieving integrated code from source repositories. These servers provide a clean and stable platform for validating code, a crucial step in ensuring it functions correctly.

The CI server is triggered whenever a developer pushes code changes to the repository. It automatically clones the repository, checks out the source branch, merges it with the mainline branch (e.g., master), and builds the code. If the build succeeds, the changes can be safely merged into the mainline branch.

Also, the CI server helps enforce coding standards, such as code formatting and test coverage requirements. It can be configured to fail the build if the code violates any predefined rules or if the test coverage drops below a specified threshold.

4. Use an Automation Testing Tool: Since automated testing is pivotal in the DevOps process, various tools are essential for running multiple tests, including unit, regression, and functional tests.

The transition from manual to automated testing, facilitated by identifying and rectifying bottlenecks, can be pivotal. Test orchestration becomes particularly advantageous when a series of tasks need to be executed in a specific order, enhancing the feedback process and ensuring swift availability of feedback to development teams.

Compared to tools like Jenkins and Bamboo, HyperExecute by LambdaTest stands out as a high-performing option in this case. HyperExecute is an end-to-end test orchestration platform that offers a streamlined and accelerated approach, up to 70% faster than any cloud grid or traditional alternatives.

5. Deploy to Different Environments: The final stage involves deploying the developed software to production or any other staging environment. This can be executed manually or automatically, considering deployment frequency, size, success metrics, and time to value.

Incorporating automation early in the development life cycle while maintaining a degree of manual testing can streamline the testing process, boost throughput, and ensure thorough coverage of the ever-growing codebase.

Factors to Consider When Selecting DevOps Tools

When choosing DevOps tools, consider features such as iterative planning, effective collaboration, support for various automation and API tools, and the ability to store configurations in version control.

• Discovering User Needs: In the discovery phase, teams collaborate to research user problems and define project goals. Tools like Mural, Miro, and Jira Product Discovery enable this ideation and prioritization process.
• Planning the Work: Once the project scope is defined, development and operations teams use project management tools like Jira and Confluence to break down the work, plan sprints, and align on requirements.
• Building the Application: Developers leverage infrastructure as code tools (Ansible, Terraform) and containerization (Kubernetes, Docker) to create production-accurate development environments. Git-based tools like Bitbucket, GitHub, and GitLab facilitate source control and collaborative coding.
• Incorporating Automated Testing: Continuous Integration and test automation are critical for rapid feedback. DevOps testing tools like Jenkins, Bamboo, etc., help execute automated tests across the codebase.
• Deploying With Confidence: With a thoroughly tested codebase, automated deployment pipelines (e.g., Bitbucket Pipelines) streamline the release process. Release dashboards in tools like Jira to provide visibility into the deployment status.
• Monitoring and Troubleshooting: Effective DevOps requires robust monitoring tools – both at the application level (New Relic, Datadog) and the infrastructure level (Splunk, Datadog). This observability empowers teams to identify and resolve issues quickly.
• Capturing Continuous Feedback: Collecting and analyzing user feedback is a crucial DevOps practice. Tools like GetFeedback and Pendo help capture insights, while social media integrations surface real-time customer sentiment.

Challenges Involved in Building a DevOps Pipeline

DevOps, a transformative automation and integration process, encounters distinct challenges that shape its implementation and evolution.

Here are some of those challenges:

• Scalability: Designing the pipeline to efficiently handle increased workloads and manage resources effectively to avoid bottlenecks and performance issues.
• Testing: Implementing comprehensive automated testing (unit, integration, functional) and maintaining consistent environments to ensure high code quality and reduce deployment failures.
• Continuous Integration and Continuous Deployment: Establishing a smooth and reliable CI/CD process that provides rapid feedback, minimizes disruptions, and includes effective rollback mechanisms for deployment issues.
• Monitoring and Logging: Setting up real-time monitoring and detailed logging to promptly detect, address, and debug issues, ensuring the pipeline’s smooth operation and performance analysis.
• Integration of Tools: Ensuring compatibility and seamless operation among various tools (CI/CD, version control, monitoring, etc.) is complex and requires configuration.
• Security: Incorporating robust security measures, including automated vulnerability scans and access control, to protect the pipeline and the codebase.

Best Practices for Managing DevOps Pipelines

When implementing a DevOps pipeline, it is essential to adhere to proven best practices that significantly contribute to maximizing the benefits of the DevOps model and seamlessly integrating it into daily operations.

• Automate Testing: Automating tests is pivotal to saving time and boosting throughput. Whether it’s unit, regression, or functional tests, automation ensures the efficient progression of error-free code through the pipeline, enhancing overall reliability.

Automated reporting and alerting of developers in case of any issues further streamlines the process, enabling prompt identification and resolution of problems.

• Multiple Test Environments: Maintain separate environments for development, testing, staging, and production. This separation of concerns allows for proper isolation and mitigation of issues before deploying code to production.

Developers use the development environment to write and test code locally. The QA environment(s) enable thorough testing and quality assurance from a user perspective, including edge-case scenarios and automated UI tests.

The staging environment validates the application’s behavior in a production-like setting with integration and chaos engineering tests. Finally, the production environment hosts the live, customer-facing application with maximum resource allocation for optimal performance and uptime.

• Integrate CI/CD Tools: Choose and integrate CI/CD tools tailored to business requirements to derive maximum benefits. Jenkins, GitLab, TeamCity, or Bamboo offer versatile solutions. Jenkins, in particular, stands out for its customizability, providing adaptability to specific organizational needs.
• Comprehensive Codebase Testing: Ensure thorough testing of the entire codebase, employing a combination of manual and automated tests. This practice guarantees identifying and resolving issues before code deployment, contributing to overall code integrity.
• Optimize Time to Value: Optimize the DevOps pipeline to achieve the lowest Time To Value possible. Accelerating product release velocity is attainable by streamlining processes and minimizing delays in delivering value to end-users.
• Enhance Collaboration: Foster collaboration between teams to promote transparency and conflict prevention. Collaboration ensures all stakeholders are on the same page, contributing to smoother workflows and reducing conflicts.

Key Metrics for Efficient DevOps Pipeline Management

Organizations embracing DevOps prioritize efficient pipeline management by closely monitoring several critical metrics. These metrics serve as performance indicators, guiding the continuous improvement of the entire process.

• Test Pass Rate: It is the ratio of passed test cases to the total. A lower rate indicates a more effective testing phase.
• Number of Bugs Encountered: It tracks the total number of bugs discovered and provides insights into code quality and potential issues.
• Defect Escape Rate: It evaluates the number of issues identified post-production compared to pre-production. A lower rate signifies better code quality and fewer post-release problems.
• Number of Code Branches: It represents the feature components introduced to the development project, aiding in managing code complexity.
• Deployment Frequency: It measures the throughput of the pipeline, indicating how often code changes are deployed.
• Deployment Size: It evaluates the efficiency of the pipeline in handling and deploying large code chunks effectively.
• Deployment Success: It assesses downtime, outages, and performance issues, ensuring a smooth deployment process.
• Mean Time to Failure/Repair/Identify: It quantifies the risk of potential pipeline failures and overall system reliability measures, emphasizing the need for quick resolution.
• Time to Value: It reflects the time taken to build and release software to production; minimizing this metric ensures that the work done swiftly generates value, maximizing the benefits of using DevOps practices.

Conclusion

The DevOps pipeline represents a transformative approach to software development, empowering organizations to accelerate delivery, enhance quality, and foster collaboration between development and operations teams.

By building an efficient DevOps pipeline, organizations can stay ahead of the curve, delivering high-quality software faster and more reliably to meet the ever-changing demands of the digital landscape.

Frequently Asked Questions (FAQs)

What are the 6 phases of the DevOps pipeline?

The number of phases in a DevOps pipeline can vary slightly depending on the specific definition, but typically there are 6 core phases:

1. Plan
2. Code
3. Build
4. Test
5. Deploy
6. Monitor

How do I run the DevOps pipeline?

Running a DevOps pipeline depends on the specific tools you’re using. Here are the general steps::

1. Use a CI/CD Tool
2. Define Pipeline Stages
3. Trigger the Pipeline
4. Monitor and Review



Chandrika Deb

Chandrika is a digital marketer with 4+ years of experience in digital transformation and technical content creation. As a freelance writer specializing in DevOps and software testing, her blogs have attracted over 500,000 views. In her free time, she enjoys binge-watching Netflix and illustrating on her iPad.

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