May 28, 2013
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.
May 7, 2013
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.
April 4, 2013
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.
December 3, 2012
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?
August 20, 2012
Hadoop has acquired a large body of prevailing myths in its short history as the hottest new big data technology.
June 29, 2012
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.
June 11, 2012
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 convergence of Hadoop and data warehousing), and tried to anchor social sentiment in the nitty-gritty of behavioral propensity. I opened up a new thread of meditation: the value of proofs of concept (POC) in the data warehousing (DW) appliance procurement process.
May 23, 2012
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:
May 21, 2012
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.
May 8, 2012
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 community, but those that combine “Analytics” and “Big Data” are getting about ten times the valuation. Netezza is no longer a start-up - we at Netezza have been helping customers with analytics and big data since our beginnings over ten years ago. And then there was that little matter of our acquisition by IBM, itself at a pretty healthy valuation. There isn’t really anything new about big data but the name. Companies have had to deal with larger amounts of data, more types of data, and faster generated or changing data since data has existed. Now because the term has gone viral, all the data management vendors are trying to wedge it into every press release and all their social media posts to catch the search engines. (Vendors in other segments seem to be looking for ways to get in on that game. Maybe we’ll see Kellogg’s “Big Data Crunch” on our supermarket shelves soon.)