Let us appreciate a nice explanation of implicit schema in databases as one might explain to a 5-year-old.

Imagine you have a big toy box. This is like your database. You can put all sorts of toys in it - dolls, cars, blocks, puzzles, etc. You don’t have to decide beforehand what types of toys you’ll put in the box. This is what we mean by “schemaless” - you don’t have to plan ahead.

Now, let’s say you start putting toys in the box. At first, you might put in a few dolls and a few cars. Later, you might add some blocks and puzzles. The types of toys you put in the box depend on what you want to play with at the time. This is like how an application adds data to the database based on its needs.

Over time, you might end up with a lot of dolls and cars in the box because those are the toys you like to play with most. This is like the “implicit schema” that emerges in the database. It’s not a rule, but it’s a pattern that develops based on what the application usually stores.

But here’s the cool part - even though you might have mostly dolls and cars, you can still add a new type of toy anytime you want. Maybe tomorrow you get a new action figure. You can just toss it in the box with the rest of the toys. The toy box doesn’t have any rules about what types of toys are allowed. This is what we mean by the database remaining flexible.

So, even though a pattern (or “schema”) might emerge over time based on what the application usually stores, the database itself never enforces these patterns as strict rules. It stays flexible, allowing the application to store whatever data it needs, whenever it needs to.

That’s basically what that phrase means - over time, the data in a schemaless database might develop a consistent structure based on the needs of the application, but the database itself always stays flexible, allowing that structure to change easily as the application’s needs change.

Based on a chat with claude-3-opus on perplexity.ai