Unravel provides full-stack coverage and a unified, end-to-end view of everything going on in your environment, plus recommendations from our rules-based model and our AI engine. Unravel works on-premises, in the cloud, and for cloud migration.

Previously Loadero had the same amount of compute power assigned to each participant in every test, but applications that our customers test are different and have different demands for end user machine resources. That is why we added compute units to give our customers better flexibility in test creation. This also allows to run much larger load tests as well as optimize cost of running the tests for the applications that don’t use much compute power.

After having announced Kuma 1.0 GA with over 70+ new features and improvements (and Kuma 1.0.1 this week), we are finally happy to announce a new major version of Kong Mesh that includes all the latest Kuma features – and more – in a fully supported enterprise package. With Kong Mesh 1.1 we can now deploy the most advanced enterprise service mesh in production across every cloud and private datacenter, on both Kubernetes and virtual machines.

As many countries face another wave of COVID-19 cases, organizations around the world are planning new ways to keep their workforces safe. Leading companies have made huge strides in managing coronavirus while keeping their workplaces open. But despite how far we’ve come, many employees still doubt that their organizations are ready to respond to COVID-19.

Retaining customers is more important for survival than ever. For businesses that rely on very high user volume, like mobile apps, video streaming, social media, e-commerce and gaming, fighting churn is an existential challenge. Data scientists are leading the fight to convert and retain high LTV (lifetime value) users.

With the complexity of data growing across the enterprise and emerging approaches to machine learning and AI use cases, data scientists and machine learning engineers have needed more versatile and efficient ways of enabling data access, faster processing, and better, more customizable resource management across their machine learning projects.