Remove 2016 Remove Big Data Remove Storage
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It's time to establish big data standards

O'Reilly Media - Data

The deployment of big data tools is being held back by the lack of standards in a number of growth areas. Technologies for streaming, storing, and querying big data have matured to the point where the computer industry can usefully establish standards. Storage engine interfaces. Storage engine interfaces.

Big Data 181
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Supercomputing Predictions for 2016

CTOvision

Wondering where supercomputing is heading in 2016? This is something to keep an eye on throughout 2016. Data-Tiering. As a result, there is now a need for managing data movement between disks to solid state storage to non-volatile memory to random-access memory. Katie Kennedy. New Processor Technologies.

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David Patterson Biography

The Crazy Programmer

Turing Award laureate David Patterson retired in 2016. He is famous for research on redundant arrays of inexpensive disks (RAID) storage. Computer security Systems design Server Real-time computing Software deployment Elasticity and information technology Storage area network Workstation. He served for 40 years.

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Cray Targets Enterprise Big Data with New Open Agile Analytics System

CTOvision

has announced the launch of the Cray® Urika®-GX system -- the first agile analytics platform that fuses supercomputing technologies with an open, enterprise-ready software framework for big data analytics. The Cray Urika-GX system is designed to eliminate challenges of big data analytics. About Cray Inc.

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The Rise of Hybrid Cloud: 7 Reasons Why It Might be a Better Choice

OverOps

The second phase of cloud evolution occurred between 2014 and 2016. For instance, AWS offers on-premise integration in the form of services like AWS RDS , EC2, EBS with snapshots , object storage using S3 etc. Higher Level of Control Over Big Data Analytics. Stage 2 – Impractical Eagerness Towards the Cloud.

Cloud 189
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How to build analytic products in an age when data privacy has become critical

O'Reilly Media - Data

One application could be medical institutions wanting to build and learn a more accurate, joint model, without sharing data with people outside their respective organizations. In 2016, Google took this “shared model” concept and scaled it to edge devices! It’s time for data ethics conversations at your dinner table”.

Analytics 181
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Big Data Fabric Weaves Together Automation, Scalability, and Intelligence

Cloudera

In conjunction with the evolving data ecosystem are demands by business for reliable, trustworthy, up-to-date data to enable real-time actionable insights. Big Data Fabric has emerged in response to modern data ecosystem challenges facing today’s enterprises. What is Big Data Fabric? Data access.