DevOps pipeline stages and how to build your pipeline

What Is a DevOps Pipeline? 

A DevOps pipeline is a set of practices and tools designed to improve the efficiency and quality of software development and deployment. It embodies the philosophy of DevOps, which emphasizes collaboration between development and operations teams to automate and integrate the processes of software development, testing, deployment, and monitoring. 

By streamlining these processes, a DevOps pipeline aims to shorten the development lifecycle, enhance product quality, and increase the frequency of deployments. This enables organizations to deliver value to their customers faster and more reliably.

This is part of a series of articles about CI/CD

In this article:

Key Principles of DevOps Pipelines

The DevOps pipeline consists of several key components that work together to automate and optimize the software development lifecycle:

1. Automation of Entire Development Lifecycle

Automation in a DevOps pipeline involves using tools and technologies to automatically manage and execute tasks throughout the software development lifecycle, from code creation to deployment and monitoring. This reduces manual effort, decreases the potential for errors, and speeds up the delivery of software. 

Automation covers various stages such as code integration, testing, deployment, and infrastructure provisioning. By automating repetitive tasks, teams can focus on more strategic work, ensuring that the software development process is more efficient, consistent, and scalable.

Learn more in our detailed guide to DevOps automation (coming soon)

2. Continuous Feedback

Continuous feedback is critical in a DevOps pipeline, enabling teams to quickly identify and address issues, improve product quality, and adapt to changing requirements. This involves the continuous monitoring of applications and infrastructure to gather insights on performance and user experience. 

Feedback mechanisms, such as automated alerts, performance dashboards, and user feedback channels, provide real-time information to developers and operations teams. This ongoing loop of feedback and improvement helps in making informed decisions, enhancing collaboration, and ensuring that the product aligns with user needs and business objectives.

3. Continuous Operations

Continuous operations aim to ensure that software applications and services are always available and perform optimally, regardless of the deployment of new features or updates. This involves practices such as automated provisioning and configuration of infrastructure, continuous monitoring, and the use of deployment strategies that minimize downtime. 

By automating operations tasks, organizations can maintain high availability and reliability of their systems, enabling them to respond swiftly to market changes and maintain a competitive edge.

The Phases of a DevOps Pipeline 

The DevOps pipeline can be broken down into distinct phases, each contributing to the overall goal of accelerating and optimizing software delivery. 

Develop

The development phase is where ideas are turned into code. Developers write and review code, leveraging version control systems to manage changes and collaborate effectively. This phase is characterized by practices such as pair programming, code reviews, and feature branching, which help to ensure high-quality code and facilitate continuous integration.

Build

In the build phase, the source code is compiled into executable programs or scripts. This stage may also involve packaging the code, along with any dependencies, into a deployable unit. Automated tools are used to execute builds, ensuring consistency and saving time. The build phase is critical for detecting compilation errors and dependencies issues early in the development cycle.

Test

Testing is an integral part of the DevOps pipeline, aimed at ensuring the quality and reliability of the software. This phase involves running a variety of automated tests, including unit tests, integration tests, and functional tests, to identify and fix defects before deployment. Continuous testing allows teams to address issues promptly and maintain a high standard of software quality.

Deploy

The deployment phase involves delivering the software to the production environment, making it available to end-users. This stage can be automated to ensure reliable and repeatable deployments, minimizing the risk of human error. Deployment strategies, such as blue-green deployments or canary releases, may be employed to reduce downtime and facilitate rollback in case of issues.

How to Build a DevOps Pipeline 

Implement Source Control

Source control, or version control, is the practice of tracking and managing changes to code. It’s the foundation of a DevOps pipeline, enabling developers to collaborate efficiently and safely on code changes. 

Git is the most widely used source control system, with platforms like GitHub, Bitbucket, and GitLab providing cloud-based repositories and collaboration features. Implementing source control involves setting up repositories for your projects, defining branch strategies (e.g., feature branching or trunk-based development), and establishing workflows for code reviews and merges.

Set Up a Build Server

A build server automates the compilation, building, and preliminary testing of code. It acts as the central point where code is integrated from different developers, built into executables, and prepared for testing. 

Jenkins, Bamboo, and TeamCity are examples of tools that can be used to configure build pipelines. These tools monitor source control for changes, execute build scripts, and report the outcome of builds. Setting up a build server involves configuring the server to work with your source control system, defining build steps, and setting up notifications for build outcomes.

Set Up Automated Testing Tools

Automating testing within a DevOps pipeline ensures that code is consistently and thoroughly vetted for issues. Tools like Selenium for web testing, JUnit for unit testing, and Cucumber for behavior-driven development (BDD) tests are integrated into the pipeline. These tools automatically execute tests against new code commits or builds, report outcomes, and sometimes even roll back changes if tests fail. 

Setting up these tools involves selecting appropriate frameworks for your project’s needs, writing test cases, and integrating these tests into your build or deployment processes.

Automate Deployment

Automating the deployment process enables consistent, reliable, and fast delivery of software to production. Tools like Ansible, Chef, and Kubernetes help automate the deployment of applications to various environments (development, testing, production). 

Automating deployment involves defining deployment scripts or configurations, setting up environment-specific parameters, and integrating these processes into the continuous delivery pipeline to support seamless, automated promotions across environments.

Implement CI/CD Tools

Continuous Integration/Continuous Deployment (CI/CD) tools orchestrate the workflow from code commit to deployment. Implementing CI/CD involves setting up a pipeline that automatically integrates code changes, runs tests, and deploys successful builds to production. 

Tools like Ocean offer functionalities to define pipeline stages, execute automated tests, and deploy applications. Configuring these tools requires defining the pipeline configuration, integrating with source control, testing tools, and deployment mechanisms.

Increase Deployment Frequency

Once all the elements are in place, it becomes possible to increase deployment frequency, moving towards more frequent, smaller releases. This practice reduces the risk of each deployment, facilitates faster feedback, and allows for quicker iteration on features. 

Achieving higher deployment frequency involves optimizing your CI/CD pipeline for speed, ensuring automated tests cover critical paths effectively, and adopting deployment strategies that minimize user impact, such as canary releases or feature flagging. This requires continuous monitoring, learning from deployments, and refining processes and tools to support quicker turnaround times.

Best Practices for Building a DevOps Pipeline 

Implementing a DevOps pipeline effectively requires adherence to certain best practices. These guidelines help ensure that the pipeline operates smoothly and delivers the intended benefits.

Incorporate Automated Testing Alongside Manual Testing

While automated testing plays a critical role in speeding up the DevOps pipeline, incorporating manual testing for complex scenarios or usability testing is essential. This combination ensures comprehensive coverage, catching issues that automated tests may miss. Manual testing allows for exploratory testing, where testers can simulate user behavior and discover potential usability issues. Balancing automated and manual testing ensures both efficiency and quality, providing a safety net that helps maintain high software standards.

Implement Continuous Monitoring and Collect Feedback

Continuous monitoring and the collection of feedback are pillars of an effective DevOps pipeline. Implementing tools that monitor the health and performance of applications in real-time allows teams to quickly identify and resolve issues, often before they impact the end-user. Continuous monitoring extends beyond operational metrics to include feedback loops from users, enabling teams to adapt and improve the product continually. By analyzing performance data and user feedback, teams can make informed decisions, prioritizing features and fixes that deliver the most value.

Ensure Collaboration Between Teams 

A successful DevOps pipeline relies heavily on the collaboration between development, operations, and other involved teams, such as security and quality assurance. Encouraging open communication and cooperation across departments helps to break down silos, fostering a culture of shared responsibility for the product’s success. This collaboration can be facilitated by regular cross-functional meetings, shared tools that provide visibility into the pipeline’s status, and a mutual understanding of goals and challenges. Ensuring that all teams are aligned and working closely together enhances efficiency, reduces errors, and speeds up the delivery of high-quality software.

Integrate Continuous Security into the DevOps Process

Integrating continuous security into the DevOps process, often referred to as DevSecOps, is crucial for identifying and mitigating security risks early and throughout the software development lifecycle. This involves automating security checks, such as static and dynamic code analysis, and integrating them into the CI/CD pipeline. By doing so, teams can detect vulnerabilities before deployment, making the software development process faster and more secure. Continuous security practices also include regular security training for developers to help them write more secure code from the start.

Build Your DevOps Pipeline with Spot by NetApp

Spot by NetApp’s optimization portfolio provides resource optimization solutions that can help make your DevOps pipeline more impactful. Here are some examples of automated actions our users enjoy on their K8s, EKS, ECS, AKS and GKE infrastructure: 

  • Autoscaling: This single word encompasses multiple procedures: knowing when to scale up or down, determining what types of instances to spin up, and keeping those instances available for as long as the workload requires. EC2 ASG’s are an example for rigid, rule-based autoscaling. You might want to get acquainted with additional K8s autoscaling methods like HPA or event-driven autoscaling. 
  • Automated rightsizing: Recommendations based on actual memory and CPU usage can be automatically applied to certain clusters or workloads 
  • Default shutdown scheduling: Requested resources can be eliminated after regular office hours, unless the developer opts out a specific cluster. 
  • Automated bin packing: Instead of having nine servers 10% utilized, gather those small workloads in one server. Bin packing can be user-specific or not, according to your security policies. 
  • Dynamic storage volume: Your IDP should regularly remove idle storage. It’s also recommended to align attached volume and IOPS with node size to avoid overprovisioning in smaller nodes. 
  • AI-based predictive rebalancing replaces spot machines before they’re evicted involuntarily due to unavailability. 
  • Data, network, and application persistence for stateful workloads, either by reattachment or frequent snapshots. 
  • Dynamic resource blending aware of existing commitments (RIs, SPs) which must be used before purchasing any spot or on-demand machines. 
  • “Roly-poly” fallback moves your workload to on-demand or existing commitments if there is no spot availability. When spots are once again available, you want to hop back onto them. 

To discover what key optimization capabilities your platform can enable in container infrastructures, read our blog post or visit the product page.