WTF Is … The INSERT INTO Statement

2 min read

In this article in Learn SQL For Data Analysis, we are going to look at creation, namely:

How we put new rows of data into our data table.

For a table that holds EMPLOYEE data this would mean we have a new co-worker.

If we are running an e-commerce store, it might be used to log a new product in our PRODUCTS table or, even better, a new sale record in our SALES table (cha ching!)

As with all of the most used SQL statements, there is a simple to remember name for this command.

When we want to put something into our destination table we use the INSERT INTO statement.

Simple enough so far? I agree. Let’s dig deeper.

With the INSERT statement we have two main ways of specifying what exactly it is we want to insert into the table.

If we want to include a record for all of the columns in the table row we write it like this:

INSERT INTO table_name
VALUES (val1, val2, val3, ...);

We can be more specific however if we don’t have a value for each individual column.

In this case we specify the column names after our INSERT INTO statement, like this:

INSERT INTO table_name (col1, col2, col3, ...)
VALUES (val1, val2, val3, ...)

We’re lapsing into dry and dusty territory here so let’s spice it up with an example.

All that glitters is gold

Say you have a database table for storing the details of famous rock guitarists.

You might keep their name, the band they are most famously identified with, their favourite type of guitar and which country they come from.

If you want to add a new record (read: guitarist) to your table you would write the following SQL code:

INSERT INTO guitarists (FirstName, LastName, FamousBand, FavBrand, FavModel, Country)
VALUES ('Jimmy', 'Page', 'Led Zeppelin', 'Gibson', 'Les Paul', 'England');

And now you have one of the pioneers of heavy rock in your database table.

Well done, you’ve added another string to your SQL bow.

Now you can put data into a table (INSERT INTO), get it back out again (SELECT) and change some of the values (UPDATE).

Check out the next lesson from my Learn SQL For Data Analysis series.

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