This past August, Big Data University (BDU) reached a milestone: 100,000 registered users! From its humble beginnings in early 2011, we continue to see rapid growth in the number of registrations. As of the time this blog post was written, we are now at 118,500 users. The chart below says it all!
Big data is a key infrastructure in the Internet of Things (IoT), but it’s far from the only piece of the fabric. As I stated here, the IoT is central to the notion of a Smarter Planet. In the coming global order, every human artifact, every element of the natural world, and even every physical
Big data technologies like Hadoop are providing enterprises a cost-effective way to store and analyze data. Enterprises are looking at using Hadoop to augment their traditional data warehouse. Compared to traditional data warehouse solutions, Hadoop can scale using commodity hardware and can be
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