Microservices are increasingly used in the development world as developers work to create larger, more complex applications that are better developed and managed as a combination of smaller services that work cohesively together for more extensive, application-wide functionality. Tools such as Service Fabric are rising to meet the need to think about and build apps using a piece-by-piece methodology that is, frankly, less mind-boggling than considering the whole of the application at once.
Most Cortex customers begin their journey by populating their service and resource Catalogs, creating their first few Scorecards, and setting up any time-sensitive Initiatives. These features help bring visibility and accountability to internally developed software, revealing consistency issues and enabling teams to act quickly to resolve.
Every so often, somebody in the tech industry proposes a new way to quantify software engineers’ productivity. Most recently, a leading consulting firm published a report that has reignited a recurring debate—is it even possible to effectively measure developer productivity, and if so, how? Whereas functions like sales and support have had rigorous metrics for years, software engineering has long been the exception.
Today, I’ll cover Shift Left Monitoring: A Pathway to Optimized Cloud Applications and how left-shifted troubleshooting of Spring Boot code issues using observability tooling can avoid production issues, unnecessary costs and improve product quality. Shift-left is an approach to software development and operations that emphasizes testing, monitoring, and automation earlier in the software development lifecycle.
To be a true system of record, your IDP needs to be a source of truth for all the data in your stack. While Cortex offers 50+ out of the box integrations, and the ability to bring in custom data, we know there are occasions where you’ll also want to visualize or take action on data sourced from other places including internally developed tools or repositories. That’s why we’re excited to officially launch the Cortex plugin framework plus UI customization. Now users can.
Microservices architecture is a software development approach where an application is built as a collection of small, loosely coupled, independently deployable services. Each service focuses on a specific business capability and operates as an autonomous unit, communicating with other services through well-defined APIs. This architectural style is often used in the context of DevOps to create more efficient, scalable, and manageable systems.
Scaling the deployment, in order to meet demand or extend capabilities, is a known challenge in many fields, but it’s particularly pertinent when scaling microservices. This article looks at the challenges of scaling microservices and examines best practices to overcome them while maintaining app quality, dev efficiency, and a good developer experience.
In our extensive guide of best ci/cd practices we included a dedicated section for database migrations and why they should be completely automated and given the same attention as application deployments. We explained the theory behind automatic database migrations, but never had the opportunity to talk about the actual tools and give some examples on how database migrations should be handled by a well disciplined software team.
Kubernetes (k8s) adoption has skyrocketed since its 2014 introduction, becoming one of the most popular open source container orchestration platforms for its power and flexibility. K8s reduce costs by improving efficiency, optimizing resource use, and eliminating redundancies. But cost savings in Kubernetes can be tricky to maintain. In fact, a 2021 survey of 178 organizations showed that costs associated with k8s can actually increase from insufficient monitoring, resulting in overspend.