With the growing popularity of cloud computing, enterprises are seriously looking at moving workloads to the cloud. There are issues around multi-tenancy, data security, software license, data integration, etc., that have to be considered before enterprises can make this shift. Even then, not all
There is all the buzz about Hadoop these days and its potential for replacing the enterprise data warehouse (EDW). The promise of Hadoop has been the ability to store and process massive amounts of data using commodity hardware that scales extremely well and at very low cost. Hadoop is good for
One of the recurring themes at yesterday’s “Big Data at the Speed of Business” launch was comsumability, which is just a fancy word for ease of use. Let’s face it, Hadoop can be hard; big data can be complicated, and there’s certainly a learning curve involved in being able to leverage most big
When thinking of use cases for big data analytics, consider your need for immediacy. Do you have the need to know now, not just the ability to know now? In other words, would you do something differently at that moment if you knew the answer immediately?
Hadoop has acquired a large body of prevailing myths in its short history as the hottest new big data technology. I'm surprised and dismayed when I see these myths propagated in leading business publications, such as in this recent Forbes article. Here now are some quick debunks of the myths in
James Kobielus recaps the quick-hit ponderings from the IBM Netezza Facebook page. He went deeper on the themes of sexy statistics, Hadoop uber-alles, smartphones as big data analytics platforms, and big data's optimal deployment model. And he opened up a fresh topic: frictionless sandboxes.
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. I went deeper on machine learning, continued my meditation on all-in-memory, put out some more Hadoop thoughts in advance of next week's Hadoop Summit (where IBM's Anjul Bhambhri will speak on
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. Clearly, I was focused on the "big" side of big data, and on the "statistics" DNA of the analytics that power big data, and on the limits of what you can in fact "optimize" with big data and analytics
Game-changing analytics applications don't spring spontaneously from bare earth. You must plant the seeds through continuing investments in applied data science and, of course, in the big data analytics platforms and tools that bring it all to fruition.
If this was a start-up, that would be good for at least $100M...
Analytics. Big Data. At a recent conference I attended, one of the keynote speakers stated that start-ups with “Analytics” in their business description are getting about two times the average valuation by the venture capital
Big data is not just about scaling your data analytics processing platforms to keep up with the onslaught of new information. Just as important, big data is about bringing together your best and brightest minds and giving them the tools they need to interactively and collaboratively explore rich
Two things before I begin:
I’ll begin this posting with a call for inputs. Below I will list a few of the most common Hadoop/Netezza co-existence deployment patterns we have seen to date. But I would like to hear from others. As you see the continuing deployment of Hadoop in the enterprise and