How election predictions and polls failed, avoid common mistakes in securing network endpoints, and understand the future of big data and emerging technologies.
- By Quint Turner
- November 9, 2016
According to Gartner, AI and new machine learning techniques will enable a new class of intelligent apps and intelligent things -- along with the emergence of so-called digital twins.
- By Steve Swoyer
- November 8, 2016
The RDBMS challenges of the 1980s are being replayed in the world of big data.
- By Luke Liang
- November 7, 2016
What does it take to be a data engineer? A background in software engineering doesn't hurt. Although the number of data engineers doubled from 2013 to 2015, that growth rate far outstripped that of data scientists.
- By Steve Swoyer
- November 4, 2016
Today read tips for landing a career in open source development, how to use analytics to improve customer interactions, and the highlights of a new benchmark study for analytics engines on Hadoop.
- By Lindsay Stares
- November 4, 2016
Great data scientists need to be open to a wide variety of perspectives.
- By James E. Powell
- November 3, 2016
How to find value in data that hasn’t been used, what each type of analytics is good for, and making predictions about the future of big data and advanced analytics.
- By Quint Turner
- November 3, 2016
See an updated consolidation of election polling predictions, plus learn how selling data can be a huge opportunity if approached correctly, how to improve the quality of data visualizations, and understand the basic history and current potential of BI.
- By Quint Turner
- November 2, 2016