Joins are not just the-scary-thing-that-pops-up-when-I-add-more-data.
Joins can feel like a puzzle, especially when you're trying to merge two datasets that share similar data fields. Imagine a Venn diagram: your main dataset is the left circle, and the dataset you want to integrate is the right circle. The type of join you choose decides which parts of this Venn diagram you'll keep.
If you need records where both data fields match, go for an inner join—this is like shading the overlapping middle section of your Venn diagram. But if your goal is to retain all records from the main dataset, regardless of whether there's a match in the other dataset, a left join is your friend. It shades in the entire left circle, including parts that don't overlap. Sure, you might end up with some null values, but remember: nulls aren't the enemy. Sometimes, they're just part of the process.
When setting up your join, think about the join conditions—how exactly you want to link your data fields. For instance, if both datasets include city names, you'd typically match the cities from the left dataset with cities in the right dataset to ensure accurate integration.
It might sound complex, but it's not as scary as it seems! With a bit of practice, choosing the right join type and setting up join conditions become second nature.