Hello there! It's great to see that you've stuck around for the next exciting topic on our journey with SQL JOINs. After introducing ourselves to INNER JOIN, LEFT JOIN, and RIGHT JOIN in the earlier lessons, we're now moving on to the fourth and final type – true to its name, the FULL JOIN.

SQL JOINs are essential when it comes to processing data, and having a command over FULL JOINs can help you effectively analyze intricate data relations. For this lesson, we will be continuing with MySQL, but remember, the understanding of SQL you garner here is transferable to other relational database management systems (RDBMS) such as PostgreSQL, SQL Server, and SQLite, with just slight differences in their syntax.

Before we plunge into FULL JOIN, let's reinforce our knowledge of JOINs. SQL JOINs enable us to merge rows from two or more tables based on a common column among them. An INNER JOIN returns rows where there is a match in both tables. A LEFT JOIN gives all records from the left table and the matched records from the right one. A RIGHT JOIN, conversely, returns all records from the right table and the matched records from the left one.

FULL JOIN in SQL straddles the territory between LEFT JOIN and RIGHT JOIN. It provides all records where there is a match in either the left table or the right one, essentially unifying the results of LEFT JOIN and RIGHT JOIN to offer a comprehensive view of your data.

This simple visual aid below can help make sense of it, where A and B are the tables we are joining and the green areas depict the results of different JOINs.

Let's put our concepts into practice using FULL JOIN to merge relevant data from Products and OrderItems:

In this query, we adopt a bifurcated strategy. The first section applies a LEFT JOIN to list all products, product prices, along with their corresponding support selection from order items. The latter section, which is essential for simulating a FULL JOIN, uses a RIGHT JOIN to cover those rows from the OrderItems table that fail to find a match in the Products table (we ensure this by checking WHERE Products.product_id IS NULL).

By joining these two parts through UNION ALL, we effectively simulate a FULL JOIN, yielding a complete view that includes all matched and unmatched records from both tables.

Note: In our scenario, no order items are linked to non-existent products, so the LEFT JOIN captures everything. However, in more complex scenarios where mismatches exist, this method of combining LEFT JOIN and RIGHT JOIN is necessary to simulate a FULL JOIN.

In the query we've just dissected, the condition WHERE Products.product_id IS NULL in the second select statement plays a crucial role. This ensures that only those records from the OrderItems table are added that are not in the Products table. By handling NULL values in this way, we ensure that our dataset is complete, which is extremely valuable for data analysts who want to cover all possible angles in their exploration.

You've now learned how to emulate a FULL JOIN in MySQL, developing queries that provide a thorough understanding of the relationships between Products and order items. Today's lesson involved a meticulous application of SQL, giving you an improved set of technical skills and a more refined critical approach.

As you move on to the practice exercises, remember the structured method of merging LEFT JOIN and RIGHT JOIN operations. This will help solidify your understanding and demonstrate how malleable SQL techniques can solve even the most convoluted data problems. Remember, continue to apply your knowledge with imagination and precision.