James Kobielus

Columnist

As IBM's big data evangelist, James Kobielus is senior program director for product marketing for big data analytics solutions. He is an industry veteran, a popular speaker and social media participant, and a thought leader in big data, Hadoop, enterprise data warehousing, advanced analytics, business intelligence, data management, and next-best-action technologies. The views expressed here are not necessarily those of IBM.

No, the data warehouse is not dead

The all-consuming future of cloud analytics

The all-consuming future of cloud analytics

An explosion of use cases is driving runaway growth in cloud analytics -- but can anyone really declare that the cloud is the omega of IT platforms?

Hadoop is probably as mature as it's going to get

Hadoop is probably as mature as it's going to get

Five years ago, Hadoop came roaring into the mainstream as the solutions to all big data problems. Now that reality has settled in, it's time for a more realistic assessment

Analytics and measurements: A recipe for sustainable food chains

Analytics and measurements: A recipe for sustainable food chains

Our food chain is full of waste, but widely distributed sensors and big data analytics together hold potential for dramatic conservation of resources

Busted! The campaign against counterfeit reviews

Busted! The campaign against counterfeit reviews

Fake reviews, either intended to trash a company or artificially inflate its standing, are poisoning the Internet. Here's how machine learning is attempting to stop the counterfeitting

Peeling back the layers of the smarter city

What's the key to making cities smarter? Extend data-driven analytic infrastructure across every aspect of urban existence

When big data is truly better

When big data is truly better

Take advantage of scale when past experience indicates greater analytic value will result. But big data is not a hammer -- nor is every problem a nail

Can a machine detect sarcasm? Yeah, right

Applying analytics to social media? Good luck -- not all words can be taken at face value. Natural language processing helps, but it's no panacea

What's machine learning? It depends on who you ask

As interest in machine learning has grown, its definition has expanded to include a panoply of techniques for automating knowledge and pattern discovery from fresh data

Big data log analysis thrives on machine learning

Huge quantities of log data generated by all sorts of devices opens immense potential for insight, but machine learning is needed to make sense of it

Too big, too small, or just right? Balancing your social connections

An MIT professor analyzes social graph data to find where influence meets connectedness -- and how to maximize it

Never put everything in one database basket, even if it's Hadoop

Those who recommend putting everything in a Hadoop data lake forget some obvious lessons of database history

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