In this tutorial, we will look into how to integrate performance testing with k6 into your Jenkins Pipeline setup. By integrating performance tests into your CI pipelines, you can catch performance issues earlier and ship more stable and performant applications to production.

Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.

Flutter blurs the lines between designer and developer and endorses a new designer developer archetype. Part designer part engineer, part Picasso and part Pascal. With ambitious designs comes the responsibility to make those designs run on the screen without losing frames. While Flutter is performant by design, how much should we really pay attention to performance optimisation? In most cases … we don’t.

To follow up on my previous article, Guide to Testing Automation with Robot Framework, I will go into more detail about the overall process of writing automated Robot Framework test cases and how to track automation in Jira using Xray. Test automation may seem hard, but it gets easier with the right tools and an open and helpful community like the one surrounding Robot Framework. If your team is using Jira, you can easily integrate testing results coming from the CI/CD pipeline.

In today’s enterprise computing landscape, multi-cloud organizations are quickly becoming the norm rather than the exception. By leveraging an API-first strategy with a microservice-based architecture, companies can achieve significant speed to market across multiple clouds. In order to achieve this, container orchestration and a well-designed CI/CD strategy are essential components in this journey.