Datameer News Page 2
San Francisco, CA — July 11, 2018 — Datameer, a leader in big data management for analytics, is pleased to announce that it has achieved Advanced Technology Partner status in the Amazon Web Services (AWS) Partner…
In today’s fast-paced world, organizations must constantly adapt and innovate to maintain their competitive advantage. Cloud Computing has revolutionized the technological landscape, allowing companies to “rent” time on online services without having rapidly-obsolescing hardware on-site.
Artificial Intelligence (AI), a theory where machines perform tasks with intelligence like humans, has been the talk of the town across all industries and for all the right reasons. AI is no longer just used to describe the sophisticated consumer profiling platforms and techniques used by the likes of Google and Facebook, Amazon, etc.
Datameer ranked among top vendors for integration and exploration, data manipulation, data governance, and administration
The modern data landscape is intense. More data is being generated than ever before along with unprecedented ways to collect and analyze it. The array of methods for using data analytics gives companies the potential to improve every angle of their organization, whether it’s detecting operational inefficiencies, generating ad-hoc analyses, or understanding their customers on a deeper level.
The Big Data & Brews video blog series continues with host John Morrell, Senior Director of Product Marketing at Datameer. The series touches on hot topics within the business of Big Data, Analytics, Internet of Things, Machine Learning, Cloud Computing, Modern BI, NoSQL and Next Generation Technologies.
The Big Data & Brews video blog series continues with host Andrew Brust, Senior Director of Market Strategy and Intelligence at Datameer. The series touches on hot topics within the business of Big Data, Analytics, Internet of Things, Machine Learning, Cloud Computing, Modern BI, NoSQL and Next Generation Technologies.
Today’s leading Cloud Platforms include numerous components for storing, processing and analyzing large volumes of data. All the basics are there: storage, analysis and processing, streaming data processing, data pipelining, data warehousing, BI and even AI. But while it’s great to have all those raw components, how do you tie them together into a comprehensive architecture? While the parts are great, they still must be assembled into the whole.