Implementing a modern, cloud-based analytics stack doesn’t have to be hard — you can do it in three steps, actually. Implementing a modern data stack (MDS) — data integration tool, cloud data warehouse and business intelligence platform — is the best way to establish a successful analytics program as data sources and data volumes multiply.
Let’s talk about SPAs. It all starts from a blank page which is subsequently filled with HTML and JavaScript. If we take PHP pages as an example, they already come bundled with the server, which is an advantage in terms of performance, right? For situations like these, server-side rendering frameworks (such as Next.js) come to the rescue. They process the code on the server-side to pre-fill the HTML result page with something (if not the whole page) before it reaches the browser.
I met Matthew in New York City about a year ago. We sat in a private conference room and he told me the story of his pharma startup. A small group of researchers set out to solve the black-box enigma of certain kinds of vicious cancers. There are so many cancers, so their vision was to focus on especially heinous ones. Fast forward to their recent FDA approval of their “Hail Mary” procedure and treatment methodology for stage-four patients of a particular cancer.
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.
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.
