The demand for digital services is growing, organizations seek new and efficient ways to bring products to market and maintain a competitive edge.
Many have turned to the DevOps method of continuous integration, continuous delivery (CI/CD). These practices is the heart of automation and continuous monitoring throughout the development life cycle, from initial coding and testing to delivery and deployment.
With CI/CD, DevOps teams can constantly iterate and release new features, and work on different features for the same application while merging code in a shared repository. This helps teams quickly detect and resolve bugs and prevents merge issues, where two or more developers can make conflicting changes and break the build. Continuous delivery takes this further, automatically deploying changes to testing or production environments, speeding up the build, test and deployment phases.
This ensures customers always have access to the latest features and accelerates the feedback loop so DevOps teams can iterate faster, improving business outcomes.
Best Practices When Adopting CI/CD
CI/CD may be an attractive idea for organizations looking to increase efficiency, innovate faster and decrease time to market, but how do they implement it successfully?
Adopting CI/CD might seem unclear at first, particularly to teams already facing time and resource pressures. However, CI/CD is a game-changer for any organization, particularly when combined with artificial intelligence (AI).
Here are some best practices to help DevOps and site reliability engineering (SRE) teams set up an unbreakable CI/CD pipeline.
Ensure End-to-End Observability
To maintain the quality of their code, DevOps and SRE teams need observability across the entire CI/CD pipeline. Implementing an end-to-end, intelligent observability solution provides code-level visibility into all software builds, apps and services in an environment, whether they’re in development or deployed to end users.
However, an observability solution is only as good as the data it captures. Therefore, teams must also ensure the platform they use captures metrics, logs and traces to reveal technical performance, as well as user experience data to provide visibility into how their builds affect business outcomes.
They should also make sure it can integrate with all the cloud platforms in their environment and that it supports key industry standards for observability, with advanced platform such as New Relic (www.newrelic.com), to eliminate potential blind spots. Teams should also prioritize automation to ensure monitoring is continuous and the observability platform can keep up with constant updates.
Automate, Automate, Automate
Any task involving manual labor is prone to human error. Automation reduces that risk and frees teams to work on higher-value activities. Identifying and automating every repeatable process in a CI/CD pipeline is a much more efficient way of working. DevOps and SRE teams can automate repeatable processes across the entire project from development to testing and deployment, including configuration changes, quality checks or dependency gathering. Not only does that reduce the risk of error, but it also allows them to spend more time focused on writing code and delivering new digital services to the business.
Adopt an SLO-Driven Approach
There is a misconception that service-level objectives (SLOs) are used only for evaluating production workloads. If DevOps teams are aiming to build high-quality, highly resilient software, they need to hold themselves accountable to the same high standard in preproduction environments. SLOs should be embedded into the CI/CD pipeline and be part of the checklist of requirements that any new build must comply with to meet the needs of key stakeholders, defined within a service-level agreement (SLA). Evaluating software quality against the same production-level SLO helps drive better quality and resiliency, which in turn leads to fewer production defects and downtime.
Continuous Quality Validation
Today, DevOps teams are often forced to search through multiple dashboards to ensure code quality objectives are being met. This process is manual, time-consuming and can be further constrained by the number and complexity of their software delivery pipelines.
It’s more sustainable and scalable to automate the quality-evaluation process to stop bad code in its tracks at every stage of the life cycle — hence continuous quality validation. The ability to amalgamate test results from various testing tools, factor in observability data from monitoring tools and automatically calculate a quality score to compare builds against predefined criteria gives DevOps teams the confidence and ability to automate the go/no-go decision for new deployments and accelerate delivery, without sacrificing code quality.
An Optimized and Intelligent Delivery Pipeline
As more organizations look to speed up delivery times and improve customer experiences, CI/CD is likely to become standard practice for all DevOps and SRE teams. By following these four best practices, those teams can improve the success of their CI/CD adoption and take their software delivery capabilities to the next level.