have to disagree with this. By forcing yourself to work with a data layer so abstracted that you can't even reference whether you're dealing with a JSON document or a set of twelve joinable tables, you're going to write the most tortured and inefficient application. Non trivial applications require leaky abstractions.

This is a conception of data that is more true in theory than it is in practice. In practice, if you want to query your data and efficiently, you'll need to worry about how it's stored. You'll have to worry about the failure cases.

Of course, it is definitely application dependent. If you're just writing a Wordpress-replacement, you can probably choose whatever data store you want and just write an abstraction layer on top of it (especially if you don't care about performance). On the other hand, if you're looking at querying and indexing terabytes (or more) of data, you'll have to work very closely with your data store to extract maximum performance.

I can sort of sympathize with this a little. I used to use MySQL for schema prototyping and then move stable stuff to PostgreSQL back when PostgreSQL lacked an alter table drop column capability.

However today, this is less of a factor. Good database engineering is engineering. It's a math intensive discipline. Today I work often with intelligent database design approaches, while trying to allow for agility in higher levels of the app.

Don't get me wrong, NoSQL is great for some things. However it is NEVER a replacement for a good RDBMS where this is needed.

How does something like MongoDB actually help with this, though? Certainly a lack of a schema lets you be more nimble in changing it, but you still have to write code to handle whatever schema you decide rather than letting a battle tested RDBMS handle it. I think NoSQLs have their uses but not forcing correctness on your data as a feature is not one of them. But I also believe in static typing.

Are you saying that Rails schema migration can only solve 10% of your migration needs? That kinda suck bro.

Rails is a bad example here as their primitive migration-system still forces the developer to write those stupid migrations by hand.

Django/South just auto-generates them which removes a huge chore from the daily development workflow.

I don't care if it is a startup or not. Come up with a very simple idea, draw the models in ER diagram, implement that stuff.

It's very hard to imagine that tomorrow suddenly all relationships need to be changed. Even if that is the case, scrap your Repository/Entity model and start from the beginning.

Nothing can help you much if the fundamentals are wrong.

I tried building a small side project with Postgres about 8 months ago (after not really doing rdbms stuff for 18 months before) and was amazed at how inflexible it felt, and how much frustration used to seem normal.

I write up a schema, send to everyone else on the team, get feedback. If users are invovled get it from them too.... take in all the feedback, write up a new draft, wash, rinse, repeat until running out of shampoo (i.e. feedback).....

Once things are pretty stable, do a prototype, address any oversights, do the real thing.

It's not rocket science.

If it prototypes well, then further refine it with ER diagrams for future maintenance.

Why is planning everything without validation better than above?

That doesn't mean spending months planning. It does mean doing your best to plan over a few days, then prototype, review, and start implementing. If things change, you now have a clearer idea of the issues and can better address them.

The worst thing you can do is go into development both blind and without important tools you need to make sure that requirements are met--- tools like check constraints, referential integrity, and the like.

large changes? throw away and start over; tools like Rails can help you get up and running really quickly.

I rarely see people go back and fix the mess.

Maintenance takes up about 50% of all IT budget [1]. Most individual pieces of software will spend 2-6 times (considering the average life cycle of an in production software product to be 2-4 years) more money on being maintained than being developed.

Data migration is a massive problem for any organization with data sets at any scale. RDBMS, in general, has gotten in the way of those migrations. People aren't looking at NoSQL just because they cannot sit still but instead are looking to find a better experience with handling data.

I'm not sure if NoSQL is the right answer to that but let's give it a chance and see what happens when people are migrating MongoDB data in 3-4 years.

[1] http://www.zdnet.com/blog/btl/technology-budgets-2010-mainte...