Operational Database Management Systems
“Big Data Security… Yeah, what about it?” by Wilco van Ginkel, *Security, November 22, 2014 As with any new trend, technology or ‘anything disruptive’, security becomes a key question. Unfortunately, most of the time,...
Getting Up to Speed on Hadoop and Big Data By Cynthia M. Saracco, Senior Solution Architect, IBM Silicon Valley Lab It’s a question I’ve often heard during my 4 years of working on Big...
Strata Spark Tsunami – Hadoop World, Part One by Merv Adrian, Research VP, Information Management, Gartner, Inc. | October 31, 2014 | New York’s Javits Center is a cavernous triumph of form over function. Giant empty spaces...
NoSQL meets Industrial Mass Data by Timo Klingenmeier, inmation GmbH & Co. KG October 31, 2014 I am writing this whitepaper at the near end of a two-year software product development cycle. It is...
The need for a data centric regulatory risk assessment framework by Ramendra K. Sahoo, Director in PwC’s Advanced Risk Analytics. The abundance of data, usage of smart devices and the demand for near real-time financial...
The Prescribed Value of Data over Time. by Anirudh Todi (Senior Software Engineer at Twitter) There is no dispute; data is growing exponentially…and it will continue to grow, both in volume and complexity. In...
(The Need for) A Global Resource Manager for Data Analytics Clusters by Ganesh Ananthanarayanan, Researcher at Microsoft Research The necessity to process large quantities of data has led to widespread adoption of data parallel...
The Biggest Opportunity is the Network Of Things. By Ken Birman, N. Rama Rao Professor of Computer Science at Cornell University. October 2014. Big Data researchers often focus on problems that start after the data...
Bringing Big Data Analytics to a Traditional DBMS. by Carlos Ordonez, University of Houston. – October 2014. Big Data is a fuzzy term with a broad meaning, used to refer to data sets so...
Data Science is mainly a Human Science: in latest KDnuggets Poll, 62% of data scientists worked only/mostly on human data. At the same time, most data scientists worked with both human and machine data....