Think of it this way. In your environment when you're working with a database. It's very possible that you're working with large datasets somewhere in the organization. They have a large dataset. Oracle databases are usually considered for environments that are working with medium to large datasets. I want to explain to you what that is. Sometimes when you're starting out, is very difficult to comprehend how large data can get how quickly we work with it every day. What we don't understand the scope. I want to try and provide you a very simple example of what a large data set might be because it's something you'll be able to relate to. Look at it this way. How many text messages do you send in a day? I'm going to narrow the number down, okay. I'm going to make it conservative judgment and say it's ten. [LAUGH] I send more than ten text messages in an hour. So let's say you send ten text messages a day. Then look at it this way. I'm thinking about the multiplier here. How many customers does your phone service provider, your cell phone service provider, your mobile phone service provider, how many customers do you think they have? We'll make a conservative guess and say that number is 5 million. It's really not very big. If global phone service providers, mobile providers, had 5 million customers They would go out of business. So let's say 5 million customers, 10 text messages a day, that's 50 million text messages a day, is it not? That's based on 10 text messages. Remember that, keep that in mind. That's based on 10 text messages a day. Many times I send hundreds of messages a day. So 50 million messages a day times 7, 350 million text messages a week, times 4 that's 1.4 billion. Text messages a month. That's just assuming that you're only sending 10 text messages, 1.4 billion messages a month. And I'm just assuming that that service provider has 5 million customers. So a lot more than that, trust me. Large mobile providers have many more than 5 million customers. And an individual sends many more than texts. 10 text messages, in a given day. 1.4 billion messages a month. You get the drift or the reasoning behind what am talking about, a large data set. This is just text messages. Now look at it this way, we were talking about 1.4 billion friends action a month, were we not? Lets make the problem even more complicated, think about compliance issues, the local government, or the federal government, or the law of the land, how many months or years does it require those messages to be preserved for? That's just assuming it's ten text messages and 5 million customers. So that's for compliance purposes what if they have to keep track of 10 years of that stuff 1.4 billion per month, times 12 months, times 10 years. You're getting the drift here of the size of the large dataset. Let's take this one step further. What can we, the service provider, what kind of intel can we gain from these text messages? Can we improve our communication systems? Can we improve our delivery system? Can we upgrade our network? Can we determine where are these text messages are coming from? How many are coming in? And what type of text messages are they? Are they carrying voice, video, images, text, what kind of data are they carrying? So Large datasets, all kinds of datasets. And yes, this is where you live in terms of an Oracle database environment. Whenever you're talking about large data sets, this is an example of how data can get very large very quickly.
