Part of the value here is to quickly add new data sources to enrich this informational dashboard. Obviously that’s happening as a batch process using the scheduler and the changes to those data models are accomplished much more rapidly. But from an actual service perspective that also integrates back through the virtualization layer with real-time data from social media, Wikipedia, Facebook, Twitter, Foursquare, Check-In, Check-Out, likes, dislikes, and Twitter activity.
So as people are navigating in real time, they can actually pull up what does Wikipedia say about that church? Oh it’s gothic architecture. That’s interesting, I am going to stop and take a look, if I am in tourist mode. Or if I am in business lunch, I am looking for offers that are contextual. Or if I am in a social mood, I can look at which of my friends, which restaurants are in this neighborhood.
So the possibilities are endless and the ability to bring those disparate types of information together is pretty critical. The goals were to easily access to disparate data, gain agility, provide real time data services, and also incorporate new feeds, and deliver high performance and scalability for large data volumes. It meant creating a hybrid approach for stable location data using a data warehouse and combine more semi-static feeds into them as a real time integration using virtualization. The benefits we have already talked about are pretty immense in this case.