If you want to fully embrace the vibe tables are difficult.
Even before LLMs, I was at a certain company that preferred MongoDB so we didn’t need migrations.
Sometimes you don’t care about data structure and you just want to toss something up there and worry about it later.
Postgres is the best answer if you have a solid team and you know what you’re doing.
If you want to ride solo and get something done fast, Firebase and its NoSQL cousins might be easier .
> Postgres is the best answer if you have a solid team and you know what you’re doing.
Not every type of data simply fits into relational model.
Example: time series data.
So depending on your model - pick your poison.
But for relational models, there is hardly anything better than postgres now.
It makes me happy coz I always rooted for the project from earily 2000s.
A really nice pattern has been to use change data capture and kafka to ship data off to clickhouse for long-term storage and analytics, which allows us to simply drop old partitions in postgres after some time.
But, for example I was working on a .net project and entity framework decided it couldn't migrate Postgres tables correctly.
I'm not a SQL person, at this point my options are to drop tables, and let .net recreate them or try and write my own migrations.
This just isn't an issue with Firebase. I can add all the fields I want directly on the client.
As soon as you have a loosely defined object you can't access any specific keys which makes it useless for 99% of times you want to store and retrieve data.
That's the entire idea behind Firebase. It makes prototyping much faster. I don't know how well it scales, but it works for most smaller projects.
It all depends on what you need to actually do. The only real weakness of Firebase is the Google lock in.
Let's say you have an object called box.
It has 3 properties, the id, it's name and it's weight.
4 months later you add a 4th property. Cost, odd records that don't have a cost just return null for that field.