Without the cloud it is very difficult to store and share the huge volumes of data needed for genome sequencing. Given the sensitivity of human genome data in terms of ownership and privacy, security and compliance are paramount.
In this post, I will show you how to make your application data persist by adding a persistent volume claim to an already deployed pod/container.
In this article I demonstrate how to set up an autoscaler to scale up the pods when the CPU usage exceeds a certain threshold and back down again.
JupyterLab is the most widely used data science / machine learning IDE. Deploying it on OpenShift / Kubernetes adds another layer of flexibility in terms of convenience, resource allocation and horizontal scaling across user groups.
At Safe Swiss Cloud, we hear from software developers time and again that with dedicated Openshift clusters, the benefits of deployment in the cloud can be perfectly exploited. Red Hat’s Platform-as-a-Service OpenShift enables faster development, deployment, monitoring and scaling of applications in docker containers. The feedback is almost always similar:
Deploying OpenShift to the cloud as opposed to bare metal, is an ideal way to get up and going quickly, being particularly well suited to development and test environments where instant resource availability and flexibility is key. A great way to smooth the path to a successful OpenShift deployment is by using automation.
A VOD (Video On Demand) page is setup on a SafeSwissCloud virtual server and the components required are discussed in the article.
While Big-Data can be compared to huge Barrels containing Water, real-time Big Data comes along as a waterfall. But not a drop may be lost. Lost real-time data is lost forever.