What is the first thing that comes to your mind when you hear the word database?

For many people, this question is more challenging than it might seem at first.

An answer, like a big file where much information is stored, is not satisfactory and would not please potential employers.

You should remember there are two main types of databases.

Relational and non relational.

The former will be the focus of this course, while the latter regards more complex systems.

Although understanding non relational databases requires a serious mathematical and programming background, some of the logic applied in its coating is the same as SQL.

Likewise, relational databases have a few advantages on their own.

A small bit of theory will explain why they are still the preferred choice in many companies and institutions.

Databases main goal is to organize huge amounts of data that could be quickly retrieved upon user's request.

Therefore, they must be compact, well structured and efficient in terms of speed and data extraction.

Today, people need such extra efficiency because data occupies memory space and the bigger its size, the more sluggish the databases and the slower the retrieval process becomes.

If we have a database containing one multimillion row table with many columns that every time a request has been received, the server must load all the records with all fields, and it would take too much time for a task to be completed.

Don't forget.

Every symbol is a container of information and requires bites of storage space.

Hence loading that much data will not be an easy job for the computer.

So what allows us to contain so much data on the server but lets us efficiently use only the portions we need for our analysis?

The secret lies behind the use of mathematical logic originating from relational algebra.

Please don't worry.

We will not bother you with math.

Imagine each table with data is represented by a transparent circle that contains all the data values of the table categorized by columns, auras.

We will often call them fields.

Now, if our database consisted of only one table, a giant circle would represent the entire database something like this huge table from our fictional example with the cells database and when we need a piece of information from the database.

For example, if we wish to see who has bought something on a certain date.

We will have to lift the whole big circle and then search for what we need.

This challenge seems vague in the process of data extraction will not be efficient.

See what can happen if we split the circle into three smaller circles, just as we did with the sales data base.

One circle will stand for the sales table, the other for customers in the last one.

For items.

There are various theoretical combinations between three or more circles, but in our database we have the following model sales, and customers have the same customer I D column and cells and items have the same item code column.

This way we can see the circles overlap as they have common fields.

So if we'd like to extract the same information the name of the customers who have purchased something on a given date, we will need on Lee the date of purchase column from the sales table in the first and last name from the customer's table.

So to satisfy this request, we will not need to lift the third circle from our database items.

This way we can save energy or more technically increase efficiency, less data represented as only two of the three circles will be involved in this operation.

The mathematical trick lies and relating the tables to one another.

Relationships were formed, namely through these common fields.

Maur explanations about this technique will be provided in our next lectures.

Anyway, I am sure that now you understand why we use the term relational data basis.

Some professionals may refer to the tables or the circles in our plot as relations, because theoretically they are the smallest units in the entire system that can carry integral logical meaning.

Likewise, the three circles are all part of our cells database.

When we combine the database and its existing relations, we obtain the famous term relational database management system, frequently abbreviated as Artie be a mess.

We hope this theoretical illustration makes things clearer.

SQL is designed for managing relational database management systems and can do that by creating relations between the different tables in the database.

Stay tuned for the next lecture, where we will outline the most substantial differences between a database in a spreadsheet.