Now that we understand a little bit about what relational databases are and

why we structure them in the way that we do.

Let's take a high level look at how to think about data.

You can think of a database table as a set of data stored to a disk.

You can add and remove dates to the set and you can update it.

When you query a table you get the whole set or even a subset of data back.

This is known as a result set.

You may have notice me saying the word settle up.

A set means a grouping of similar things and

they are the foundation of performing useful queries in relational databases.

Let's take a look at a few examples of some simple sets.

Remember, a set means a grouping of similar things together.

You could have a set of things you can wear.

Shirts, shoes, socks, coats, or you could have a more specific set of shirts.

T-shirt, dress-shirts, sweaters.

Items like shoes and socks belong in the first set but not the second set.

You can wear socks and shoes but they aren't types of a shirt.

Or you could have a set of fruits, apples, bananas, strawberries, pears.

Or sets of apples, granny smith, fugue, red delicious.

In this example, the definition of the second set, apples,

excludes items from the more inclusive set of all fruits.

You can also depicts sets conceptually as graphs called the Venn diagrams.

These graphs help us show whereas one set might overlap with another or

why one set is distinct from other sets.

Let's say we have a group of people who like certain types of fruits and

we want to show who likes what.

We can depict this with Venn diagrams.

Here we have two small sets.

The sets of people who like apples and the sets of people who like bananas.

The people who like apples are John, Stacy, Indira and Bob.

And the people who like bananas are Indira, Bob and Dante.

Now let's look at a common element for both sets.

In this case, the elements are people.

As we overlay the two sets you can see that both Bob and

Indira are the two people who like both apples and bananas.

This area in the middle is called the intersection between two sets.

The entirety of both circles combined,

in this case all people who like apples or bananas, is called the union.

Finally, what if we want to just find the people who like one fruit or

the other but not both.

This is called an except, all people who like apples, and

all people who like bananas except the people who like both.

When we perform an intersection, union or

except on sets of data, these are known as set operations.

Remember, tables and the results from queries a called data sets.

When you write a query for a port or

dynamic application, you're actually working with datasets.

That can have set operations performed on them.

Remember that a set is a collection of similar things, a data set

is nothing more than a collection of rows with the same column definitions.

A table is a data set that is stored physically on a disk.

Let's take a look at our Venn diagram example again, but

this time let's use data.

Here we see two small tables, our group of people who like apples is in

one table and then the group of the other people who like bananas in another.

Let's analyze that data for the people who like both apples and bananas.

This is the intersection from our Venn diagram a minute ago.

Now, let's see all people regardless of which fruit they like.

This is the union from our Venn diagram.

And now let's look for just people that like one fruit or the other, but not both.

This is the except example from a minute ago.

This normalization process helps databases perform set operations of intersection,

union, and except in fast and efficient ways.

As we get deeper into this course, we'll study this in more detail.

Keep the image of a Venn diagram in your mind to help you visualize the concepts.