**Heads up!** To view this whole video, sign in with your Courses account or enroll in your free 7-day trial.
Sign In
Enroll

Start a free Courses trial

to watch this video

Now that we understand a little bit about what Relational Databases are and why we structure them the way we do, let’s take a higher-level look at how to think about data.

**Set Theory** was founded in 1874, and is a relatively new mathematical discipline, as compared to Algebra or Calculus.

For more history and info about the topic, check out the Wikipedia page on Set Theory

This link takes a look at Set Operations in SQL, but we’ll be covering this more in a later stage.

Now that we understand a little bit about what relational databases are and 0:00 why we structure them in the way that we do. 0:04 Let's take a high level look at how to think about data. 0:07 You can think of a database table as a set of data stored to a disk. 0:11 You can add and remove dates to the set and you can update it. 0:15 When you query a table you get the whole set or even a subset of data back. 0:20 This is known as a result set. 0:26 You may have notice me saying the word settle up. 0:29 A set means a grouping of similar things and 0:31 they are the foundation of performing useful queries in relational databases. 0:34 Let's take a look at a few examples of some simple sets. 0:39 Remember, a set means a grouping of similar things together. 0:44 You could have a set of things you can wear. 0:48 Shirts, shoes, socks, coats, or you could have a more specific set of shirts. 0:50 T-shirt, dress-shirts, sweaters. 0:56 Items like shoes and socks belong in the first set but not the second set. 1:00 You can wear socks and shoes but they aren't types of a shirt. 1:05 Or you could have a set of fruits, apples, bananas, strawberries, pears. 1:09 Or sets of apples, granny smith, fugue, red delicious. 1:14 In this example, the definition of the second set, apples, 1:18 excludes items from the more inclusive set of all fruits. 1:22 You can also depicts sets conceptually as graphs called the Venn diagrams. 1:28 These graphs help us show whereas one set might overlap with another or 1:33 why one set is distinct from other sets. 1:37 Let's say we have a group of people who like certain types of fruits and 1:41 we want to show who likes what. 1:45 We can depict this with Venn diagrams. 1:48 Here we have two small sets. 1:51 The sets of people who like apples and the sets of people who like bananas. 1:53 The people who like apples are John, Stacy, Indira and Bob. 1:58 And the people who like bananas are Indira, Bob and Dante. 2:05 Now let's look at a common element for both sets. 2:10 In this case, the elements are people. 2:14 As we overlay the two sets you can see that both Bob and 2:17 Indira are the two people who like both apples and bananas. 2:22 This area in the middle is called the intersection between two sets. 2:26 The entirety of both circles combined, 2:31 in this case all people who like apples or bananas, is called the union. 2:34 Finally, what if we want to just find the people who like one fruit or 2:41 the other but not both. 2:46 This is called an except, all people who like apples, and 2:48 all people who like bananas except the people who like both. 2:52 When we perform an intersection, union or 2:58 except on sets of data, these are known as set operations. 3:01 Remember, tables and the results from queries a called data sets. 3:06 When you write a query for a port or 3:10 dynamic application, you're actually working with datasets. 3:12 That can have set operations performed on them. 3:17 Remember that a set is a collection of similar things, a data set 3:20 is nothing more than a collection of rows with the same column definitions. 3:25 A table is a data set that is stored physically on a disk. 3:30 Let's take a look at our Venn diagram example again, but 3:35 this time let's use data. 3:38 Here we see two small tables, our group of people who like apples is in 3:41 one table and then the group of the other people who like bananas in another. 3:46 Let's analyze that data for the people who like both apples and bananas. 3:52 This is the intersection from our Venn diagram a minute ago. 3:57 Now, let's see all people regardless of which fruit they like. 4:01 This is the union from our Venn diagram. 4:06 And now let's look for just people that like one fruit or the other, but not both. 4:08 This is the except example from a minute ago. 4:14 This normalization process helps databases perform set operations of intersection, 4:17 union, and except in fast and efficient ways. 4:23 As we get deeper into this course, we'll study this in more detail. 4:27 Keep the image of a Venn diagram in your mind to help you visualize the concepts. 4:31

You need to sign up for Treehouse in order to download course files.

Sign up