IBM continues to increase support for open source technologies. Today, we are pleased to announce that Cloud Pak for Data System now features a new capability for Postgres workloads—the IBM Performance Server for PostgreSQL.
In a previous blog, I explained how data science capabilities, massive parallel processing (MPP)
and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?
Research firm Quark +
IBM’s integrated platform for Data & AI, which is 100% complimentary to Red Hat offerings. It runs on OpenShift today and has a hardware version called Cloud Pak for Data System. The beauty of Cloud Pak for Data is that it includes all of IBM’s strategic Data and AI services – including Watson
Today, IBM announced bold new moves that transform our software portfolio to be cloud-native and delivered as pre-integrated solutions called IBM Cloud Paks.
A key part of this effort is Cloud Pak for Data, a cloud-native, container-based data platform that enables IBM Watson to run Anywhere,
“In 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity,” according to Gartner. It will do so largely by learning how to make better predictions over time and supplementing people’s ability to complete tasks in more natural ways
It’s no surprise: most companies working with stream data today say they are planning to make changes to drive greater value. Advancements in machine learning (ML) and very-high-speed data persistence for real-time analytics are reshaping strategies and architectures. In addition, 88 percent of
In business, aspiring to world-class is not enough when your competitors are already there. About half of the companies listed on the S&P 500 will be replaced over the next 10 years. Compared to the past, what’s unique about the disruption happening today is the rapid pace of change. During
Capitalogix is a hedge fund, but it’s really a data science firm in disguise. They work to understand and exploit capital markets by building custom data science models that can analyze massive amounts of data from as many sources as possible. Capitalogix’s need for high-performance analytics and