InfoQ Homepage Automated Deployment Content on InfoQ
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Lessons Learned from Enterprise Usage of GitHub Actions
GitHub Actions is an effective CI tool. However, integrating it into enterprise organizations can be challenging. This article looks at best practices for GitHub Actions in the enterprise.
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Blue-Green Deployment from the Trenches
Introducing blue-green deployments is often a beneficial improvement. However, with some architectures, it can be challenging to make the changes without impeding deployments. This article covers the challenges and lessons learned in implementing blue-green deployments in the real-world.
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The Importance of Pipeline Quality Gates and How to Implement Them
A quality gate is an enforced measure built into your pipeline that the software needs to meet before it can proceed. This article covers how to get the maximum benefit from quality gates. Making good use of quality gates not only can improve the quality of your software, but it can also improve your delivery speed.
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Bringing a Product Mindset into DevOps
To be successful, organisations need two things: products and services their customers find valuable, and the ability to deliver these products and services well. This article shows why we must design, implement and operate our delivery pipelines (the means of turning ideas into products in the hands of users) as we would any other product or service: by adding a “product mindset".
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Why Observability Is the Key to Unlocking GitOps
In a GitOps work process, Git is the single source of truth for the system’s intended state. Observability can provide the missing piece: the single source of truth for the system’s actual state.
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Successfully Integrating Dynamic Security Testing into Your CI/CD Pipeline
Dynamic security testing tools don’t require advanced cybersecurity knowledge to operate. Integrating DAST into your CI/CD pipeline should be done in stages by focusing on the riskiest areas first.
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Why is Everything So Slow? Measuring and Optimising How Engineering Teams Deliver
As teams grow, they will slow down, but it should not mean that teams stop delivering value that can power future business growth. Avoiding excessive technical debt and ensuring systems are secure and performant becomes increasingly important. As an engineering leader, you can do things to be confident that your team is moving at the fastest and most sustainable pace.
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How Development Teams Can Orchestrate Their Workflow with Pipelines as Code
Infrastructure as Code was just the beginning. Configuration as Code followed shortly after – again becoming extremely commonplace and enabling organisations to scale their engineering capacity by a number of times. And in order to continuously increase the value development teams generate, Pipelines as Code is the natural consequence.
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Embracing Cloud-Native for Apache DolphinScheduler with Kubernetes: a Case Study
This article shares how Apache DolphinScheduler was updated to use a more modern, cloud-native architecture. This includes moving to Kubernetes and integrating with Argo CD and Prometheus. This improves substantially the user experience of deploying, operating, and monitoring DolphinScheduler.
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Sustaining Fast Flow with Socio-Technical Thinking
To sustain a fast flow of changes over long periods of time, organizations address both the social and technical, socio-technical, aspects of reducing complexity. Examples are incentivising good technical practices to keep code maintainable, architecting systems to minimize dependencies and maximize team motivation, and leveraging platforms to preclude whole categories of infrastructure blockers.
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Continuous Portfolio Management as a Contributor for Achieving Highly-Aligned, Loosely-Coupled Teams
There is a business need for fast software delivery in order to frequently test business hypotheses and drive development based on the resulting feedback. Organizations need to rapidly decide on what to build next, using a short feedback loop that greatly reduces the risk of running on untested assumptions for too long. This article explores a journey towards continuous portfolio management.
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Using Machine Learning for Fast Test Feedback to Developers and Test Suite Optimization
Software testing, especially in large scale projects, is a time intensive process. Test suites may be computationally expensive, compete with each other for available hardware, or simply be so large as to cause considerable delay until their results are available. The article explores optimizing test execution, saving machine resources, and reducing feedback time to developers.