This talk focusses on the new horizontal scaling feature, TimesTen Scaleout, introduced in the TimesTen 18.1 release. TimesTen Scaleout extends the mature and well established TimesTen In-Memory Database by enabling it to be deployed as a distributed, elastically scalable, in-memory database offering a compelling feature set and breakthrough performance for both existing and new applications.
Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers are able to find hidden insights without the help of explicit programming. These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats.
Deploying Big Compute applications can lead to a wide range of tools and approaches needed to run large-scale applications for business, science, and engineering using a large amount of CPU and memory resources in a coordinated way. Typically Big Compute implies one of two things. It can be ordinary computing scaled across a massive parallel cluster, or it can be High-Performance Computing (HPC). The problem with the former is that it can only scale so far. And, for it to really succeed, the data itself must be scaled just as wide.
Big data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the next few years. As big data initiatives mature, organizations are now combining the agility of big data processes with the scale of artificial intelligence (AI) capabilities to accelerate the delivery of business value.
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