I don’t know if you read about this before the holidays, but I got
Category: Data & Analytics
Data becomes so complex that it is nearly impossible to work with existing data infrastructure and analysis techniques. My suite of data technology includes: 1) Real-Time Stream Processing (providing in-memory analytics, ETL, and CEP); 2) Ad-Hoc NoSQL Query (providing interactive fine-grained analytics); and 3) Batch Processing (providing off-line analytics over large volumes). Talk to me about he world of SQL vs. NoSQL, proprietary vs. open source.
My experience in analytics started when I spearheaded a data mining workbench at Teradata in 1996 that included machine learning algorithms including rule induction, decision tree, kohonen clustering, deep learning neural networks, and a host of others. I’ve been a student of ML and AI since 1988, and using it across large datasets over two decades before it became a huge focus of innovation.
MapReduce vs. RDBMS People think that MR is this new transformative technology…..new? No. Transformative? Yes.
This Big Data use-case involves a Global Fortune 100. The company is interested in rethinking
Cloud-based PaaS is pretty high on the hype curve. I’ve been of the opinion that
For those familiar with the Fortune 1000 enterprise data warehouse reference architecture, you’ll appreciate
“Sensor” applications have great potential in the Big Data space. The fact that machines produce
Are there any Big Data “killer apps”? The quick answer is “No”. The Hadoop framework is
The best Big Graph problems will involve the following characteristics: Discovery-centric problems (little is known
When I think of managing “Big Data”, I frequently think back to the graphic
I’ve been very intrigued by graphs and their potentially broad application leveraging Big Data and