In this contributed article, Kieron-Sambrook Smith, Chief Commercial Officer for Platform.sh, discusses why enterprises tend to lose the forest (business goals) for the trees (container management), and the strategies they can implement to stop distracting their developers from doing what they do best: writing killer code.
insideBIGDATA Guide to Data Platforms for Artificial Intelligence and Deep Learning
November 8, 2018 by Leave a Comment
This insideBIGDATA technology guide explores how current implementations for AI and DL applications can be deployed using new storage architectures and protocols specifically designed to deliver data with high-throughput, low-latency and maximum concurrency.
Five Data Platform Considerations When Thinking About Your Deep Learning Future
March 12, 2018 by Leave a Comment
With the current maturation of Artificial Intelligence applications and Deep Learning algorithms, many organizations are spinning up initiatives to figure out how they will extract competitive differentiation from their data. This guest article comes from DDN Storage, a provider of high performance, high capacity big data storage systems, processing solutions and services to data-intensive, global organizations.
Cazena Announces New Data Science Sandbox as a Service
February 23, 2017 by Leave a Comment
Cazena unveiled its Data Science Sandbox as a Service. The service is designed to deliver significantly faster outcomes from data science and analytics programs. Now, data scientists can run a wide range of analytics in a flexible cloud environment without having to build, manage or maintain the underlying technology.
Talena Enhances Security Features and Expands Cloud Support
November 4, 2016 by Leave a Comment
Talena, the always-on big data pioneer, enhances its award-winning big data management platform with expanded cloud support and key security features.
Big Data Platforms: Eckerson Group
September 1, 2016 by Leave a Comment
At the heart of a big data environment is a big data platform, which does all the heavy lifting recquired to capture, store, transform, and govern large volumes of multi-structured data at high speeds. Big Data platforms process data in batch or real time using both relational and non-relational database engines, languages, components, and techniques.
