Curated articles, resources, tips and trends from the DevOps World.
eBay sponsored this post. Tanya Vlahovic Tanya leads the Developer Ecosystem at eBay. Prior to leading the team, she worked as a lead architect for public APIs. She is one of the key contributors to revamping eBay's public API program.
At Smarking, we use the Amazon Web Services (AWS) infrastructure. We help organizations improve the efficiency of parking lots, and to do that we need to communicate with their computing systems.
Regression testing checks if changes made to a system negatively impacted or broke any of the existing features. It is often performed right after each update or commit to the code base to identify new bugs and ensure that your system works properly.
We recently open sourced our tool for Selenium testing called Callisto, so we wanted to share our experience with its development and usage. Callisto is an open-source Kubernetes-native implementation of Selenium Grid.
The promise of low-code development has long been the ability to develop business-critical applications by humans with little programming experience. In fact, over the past year or two, the lines between low-code and no-code have become increasingly blurred.
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Some Java code tools recommended by developers include DeepSource, SonarQube, SpotBugs, and more. The best way to protect your Java code from avoidable bugs is to use static code analysis tools that can help you find and fix problematic code before it reaches production.
If you have a largescale migration to AWS on the cards, you’ve probably heard of AWS’ Migration Acceleration Program. Designed to help enterprise level businesses coordinate a seamless and effective move to the cloud, it comprises three phases: Each phase has an important part to play.
In this article, we will talk about why it is necessary to optimize the payment page, making it as beautiful and convenient as possible. Do you know what mistake many companies make when selling their products and services online? They focus too much on their product.
While working as a data scientist at Microsoft, Dmitry Petrov decided that big, monolithic data platforms weren’t the way to go. There needed to be tools built on top of platforms, they needed to be open source and that machine learning engineers had particular needs not being met.
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