So far, we've learned a lot about relational databases.
We learned a lot about the model.
We learned a lot about how it's put together and what rules there are in place.
But you may be asking yourself, hey, what are these used for?
What kind of databases can we make with these?
Well, I'm here to tell you there are two different uses that are distinctly grouped for relational
databases.
One supports the day to day operations of a company and the other supports the analysis of a company.
Now, what do I mean by this?
Well, when you have a company, there are two distinct factions in a company.
Now, let's think of an Amazon.
Well, in an Amazon, you have the day to day sales people coming in, logging on, buying things,
and you have databases that are supporting those day to day transactions.
Let's keep a log of what they ordered.
Let's keep a log of what credit cards they'll use, all different types of things that support the day
to day business.
But then you have this entire back office that is supporting the analysis.
So they have all of this data on you and they're trying to figure out, hey, what makes this customer
tick?
What are they interested in?
And then you have this entire faction of the company that's taking this day to day data and doing analysis
on it to give you recommendations, to give you all of these different types of things.
And as I already said, they're grouped into two distinct buckets.
Well, if we went and we looked at the supporting day to day, we call that AOL Teep online transaction
processing.
These are databases that are driving the day to day transactions.
They have the users, they have the orders, they have the purchases.
And all they do all day is store and categorize this data in that relational model.
And so the users can go and they can select that raw data transaction processing.
So the transactions that we do, the back and forth and then the second type is called online analytical
processing or OLAP for short.
And so that's when you take what was at that front office, that transactional processing, and you
take the data and you put it in a data warehouse and then you start doing all types of analytical processing
on that data to figure out what is making that data tick.
What is valuable about that data?
Figuring out what can we do with that?
Because data is key, data is everything we work with, everything you do online, every interaction,
every action you do that is used somewhere, somehow to drive analytics that drives a company to make
decisions.
And those decisions then lead to new products, new markets, new interactions with their customers.
And that is what OLAP is used for versus old TPE.
Old TPE is driving the day to day business.
That data that you need and OLAP online analytical processing is driving those future decisions.
Knowing all of this, knowing now how relational databases work, let's kick it off.
Let's get into the dirty.
Let's get querying.