ClearScale Blog

Machine learning-enabled medical image analysis. Prosthetics customized for individual patient comfort and performance courtesy of 3-D printing. Context-aware, real-time communication platforms. Virtual Reality (VR) tools that enable medical students to experience and learn from life-and-death scenarios in low-stakes environments. They’re among the numerous examples of technology pushing healthcare to new levels of efficiency and sophistication. In many cases, the technologies driving these innovations are cloud-based. That’s good and, in the minds of some healthcare organizations, not so good

It’s a common problem. You can’t seem to develop and deploy applications fast enough to meet your customers’ changing demands. By automating the processes involved, however, you can speed things up. You’re also able to scale up and down on the fly, reduce manual errors, improve the overall availability of the application and realize the cost benefits of on-demand compute. Sounds great, right? But as Tom Hanks’ character said in the movie, A League of Their Own

Research and development (R&D) at most companies is a collaborative process. It requires working with and analyzing large amounts of data, often by teams spread out across multiple geographies and time zones. In many cases, companies rely on enterprise resource planning (ERP) applications to help them integrate and visualize the entire R&D process from idea to production. It’s essential for both the data and applications to always be accessible, secure and free from corruption or

Across all industries, automation is radically transforming the way business is conducted. Time-consuming tasks that once required an army of human beings can now be completed via automation technology. Just some of its many benefits include cost savings, greater operational efficiency and the chance to move human capital to tasks better suited for human intelligence, such as problem-solving. Organizations that can find new ways of executing workflows through automation are poised to become leaner, faster and more intelligent. The Challenge

The Challenge A financial analytics company's data analysis application had proved highly successful, but that success was also a problem. With a growing number of isolated data centers generating constant data streams, it was increasingly difficult to efficiently gather, store and analyze all that data. The company knew a cloud-based big data analytics infrastructure would help, specifically a data ingestion pipeline that could aggregate data streams from individual data centers into a central cloud-based data storage. Among the challenges in