One of the queries I try to avoid most times in SQL is the subquery. Not the simple ones, the ADVANCED Subquery. Lol. Joins are quite easy to work on.

A subquery is a query that is nested inside a SELECT , INSERT , UPDATE , or DELETE statement, or inside another subquery.

When I am not working on a real dataset for clients, I do go on kaggle to get open dataset to work on.

I am gonna be sharing with you joins and subqueries I just worked on. It's ADVANCED but not deep like that. Lol.

This a Game dataset with two different tables.
(I) ps4_gamessales table
(II) xboxone_gamesales

The picture below shows how I created the database "Game_sales" and the joining of both tables and the result.

2) The pictures below shows filtering of the joined tables using the where, Group by and order by clause.

3) More filtering like the picture above but with # in it. The # in front of a query means that line won't be executed. Instead of writing queries over and over again, using the # kinda helps and saves times. All I need to do is put # in font of the queries I Don't want executed.

4) This pics below shows Subquery using the From clause. The first pics (result) of this query gives you the not common genres and publishers from both tables and the second pics shows the common genres and publishers from both tables.

5) This pics shows Subquery using the where clause. The result of this query gives you genres and publishers not in the common genres and publishers from both tables. Hope you understand. Lol.

This is me just messing around with joins and subqueries.