Chris Raphael

As Oracle recounts, Apache Spark excels at running machine learning queries on massive data sets.

First, identify the data and brainstorm a use case. Then make sure everything’s in place to make it work.

Some of the most successful companies these days have mastered “intelligent integration,” in which applications can analyze and route data from multiple systems in pursuit of a business result.

Why trying to analyze big data in the form of CSV and TSV files can be a colossal waste.

Designing IoT products for the healthcare industry requires focusing on real value.

TimescaleDB, which is optimized for complex queries on time-series data, says it can solve some pressing challenges with IoT data analysis.

“Interest in artificial intelligence is surging.”

The Blockchain Intelligence Group focuses on detecting illegal cryptocurrency transactions.

Fog computing appears to be taking shape.

Fresh off his role leading Plat.One to acquisition by SAP, Bates has found a new home at a leader in testing automation.

IBM sees benefits in using machine learning where most enterprise data resides.

Can real-time analytics be leveraged for disaster response? A leading innovator in the field outlines strategies and lessons learned.